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Top 10 algorithms in data mining research papers 2014

This paper presents the top 10 data mining algorithms These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, In an effort to identify some of the most influential algorithms that have been widely used in the data mining community, the research International Conference on Data Mining identified the top 10 algorithms in data mining for presentation at ICDM ’06 in Hong Kong. As the first step in the identification process, in September 2006 we invited the ACM KDD Innovation Award and research ICDM Research Contributions Award winners to each nominate up to 10 best-known algorithms in data mining. All except one in this distinguished set of award winners responded to our invitation. We asked each nomination to provide the following information: (a) the algorithm name, (b) a brief justification, and (c) a representative publication reference. We also advised that each nominated algorithm should have been widely cited and used by other researchers in the field, and the nominations from each nominator as a group should have a reasonable representation of the different areas in data mining

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data mining research papers 2014

Privacy Preserving Data Mining Using Piecewise Vector Quantization (PVQ)
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Abstract Over the last twenty years, there has been an extensive growth in the amount of private data collected about individuals. This data comes from a number of sources including medical, financial, library, telephone, and shopping records. Such data can be

Application of Higher Education System for Predicting Student Using Data mining Techniques
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ABSTRACT The aim of research paper is to improve the current trends in the higher education systems to understand from the outside which factors might create loyal students. The necessity of having loyal students motivates higher education systems to know them well,

Data Mining Based on Principal Component Analysis: Application to the Nitric Oxide Response in Escherichia coli
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This work evaluates a recently developed multivariate statistical method based on the creation of pseudo or latent variables using principal component analysis (PCA). The application is the data mining of gene expression data to find a small subset of the most

Review on Data Warehouse, Data Mining and OLAP Technology: As Prerequisite aspect of business decision-making activity
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Abstract This paper describes the technology of data warehouse in decision making and tools for support of these technology. Data warehousing and on-line analytical processing (OLAP) are prerequisite aspects of decision support, which has increasingly become a

Educational Data Mining: An Advance for Intelligent Systems in Education
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Computer-based technologies have transformed the way we live, work, socialize, play, and learn. Today, the use of data collected through these technologies is supporting a second- round of transformation in all of these areas. Over the last decades, the methods of data

Data mining in social networking sites: A social media mining approach to generate effective business strategies
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ABSTRACT Mining social media is a new plan to boom business. The Social media houses vast amount of usergenerated data which can be used for data mining. Marketing enthusiasts are searching for means to utilize these mined business information for the

Assessment of Robust Learning with Educational Data Mining
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Abstract Many university leaders and faculty have the goal of promoting learning that connects across domains and prepares students with skills for their whole lives. However, as assessment emerges in higher education, many assessments focus on knowledge and

Educational data mining and learning analytics
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During the last decades, the potential of analytics and data mining methodologies that extract useful and actionable information from large datasets--has transformed one field of

Data mining application in biomedical informatics for probing into protein stability upon double mutation
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ABSTRACT To explore the mechanism of protein stability change is one of the important topics in protein design. The accurate prediction of protein stability change upon mutation is very useful for enhancing the experimental efficiency in many biological and medical studies.

Recommendation-based modeling support for data mining processes
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Abstract RapidMiner is a software tool that allows users to define data mining processes based on a visual model and implements a variety of so-called operators for data extraction, manipulation, model learning and analysis. The large number of available

Accepting or Rejecting Students' Self-grading in their Final Marks by using Data Mining
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Abstract In this paper we propose a methodology based on data mining and self- evaluation in order to predict whether an instructor will or will not accept the students' proposed marks in a course. This is an on-going work in which we have evaluated the

Applying Data Mining Technology to Solve the Problem of Traffic: A Case Study
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ABSTRACT Traffic rules assure the safety and efficiency of transportation systems. In this paper, we establish three models to analyze the performance of traffic rules. Model deals with the traffic rules in single-lane. We simulate the vehicles movement and focus on

DRIP–Data Rich, Information Poor: A Concise Synopsis of Data Mining
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Abstract As production of data is exponentially growing with a drastically lower cost, the importance of data mining required to extract and discover valuable information is becoming more paramount. To be functional in any business or industry, data must be capable of

Logistic Regression in Data Mining and its Application in Identification of
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Abstract Data mining in clinical medicine deals with learning models to predict health of patients. The models is used to support clinicians in therapeutic or monitoring tasks. Data mining techniques are usually applied in clinical contexts to analyze retrospective data,

Drugs Highly Associated with Infusion Reactions Reported using Two Different Data-miningMethodologies
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A Literature Review on Kidney Disease Prediction using Data Mining Classification Technique
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ABSTRACT -The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information

E-learning and educational data mining in cloud computing: an overview
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ABSTRACT E-learning is related to virtualised distance learning by means of electronic communication mechanisms, using its functionality as a support in the process of teaching- learning. When the learning process becomes computerised, educational data mining

A Literature Review in Health Informatics Using Data Mining Techniques
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Abstract In this paper we present an overview of the applications of data mining in administrative, clinical, research, and educational aspects of Health Informatics. The current or potential applications of various data mining techniques in Health Informatics are

Mining Latent Entity Structures from Massive Unstructured and Interconnected Data
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Mining Latent Entity Structures from Massive Unstructured and Interconnected Data Jiawei Han Department of Computer Science University of Illinois at Urbana-Champaign Urbana, IL,

Prediction of agricultural gross domestic product in Thailand using data mining
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Abstract In this study, the agricultural gross domestic product (GDP) of Thailand was predicted as a function of crop production index using econometric and data mining methods. The data set used herein was obtained from the Bank of Thailand, which

Data-Mining in Toxicology
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Abstract Literature searches are necessary to find answers to many toxicological issues. Fortunately, today we are no longer reliant on time-consuming searches in reference books, but can make use of the Internet as an important tool for gathering information. A lot of

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ABSTRACT With the rapid development of the medical science more and more medical images are generated rapidly like MRI, CT scan, X-ray etc. Due to that an efficient system is essential for the indexing, storing and analyzing such images. The analyzing cost of such

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ABSTRACT Almost, each and every real time process is being automated in today's competitive world of technological advancements. Automation has become the Blood Line of life. Data Mining is one of the powerful automation tools, as it has evolved from the

Customer Behavior in Banking Industry: Comparison of Data Mining Techniques
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ABSTRACT Nowadays organizations have perceived the importance of managing customer relationship and its potential benefits. Customer relationship management supports organizations to deliver beneficial relations with customers. Customer satisfaction and

Data Mining the MOTESS-GNAT Surveys as a Source of Double Star Observations
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ABSTRACT New measurements of eleven double stars selected from the Washington Double Star Catalog are presented. The measurements were made using archival images that formed the basis of the MOTESS-GNAT variable star catalog. In addition to these new

Result Analysis of Mining Fast Frequent Itemset Using Compacted Data
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Data mining and knowledge discovery of database is magnetizing wide array of non-trivial research arena, making easy to industrial decision support systems and continues to expand even beyond imagination in one such promising field like Artificial Intelligence and facing the

Data mining with background knowledge from the web
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Abstract Many data mining problems can be solved better if more background knowledge is added: predictive models can become more accurate, and descriptive models can reveal more interesting findings. However, collecting and integrating background knowledge is

Building an intelligent pal from the tutor. com session database-phase 1: data mining
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Abstract In this poster, we describe a new research project involving the analysis of nearly 250,000 human-human tutorial dialogue transcripts (in Algebra and Physics) supplied by Tutor. com, a leading provider of online tutorial services for children and

Data mining: a tutorial
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Recommender system find and summarizes patterns in some structure (and those patterns can include how, in the past, users have explored that structure). One way to find those patterns is to use data mining algorithms. The rest of this book focuses specifically on

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Today it is exceptionally essential to give an abnormal state security to secure profoundly touchy and private data. The Intrusion Detection System (IDS) is a vital innovation in Network Security. These days scientists have intrigued on interruption discovery

An Outlier detection approach with data mining in wireless sensor network
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Abstract Wireless sensor networks had been deployed in the real world to collect large amounts of raw sensed data. However, the key challenge is to extract high level knowledge from such raw data. Sensor networks applications; outlier/anomaly detection has been

Use of Bayesian Models, Markov Models and Data Mining in Intelligent Tutoring Systems
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Student Modelling, for example in Andes Bayesian network is used to do long-term assessment of the student's domain knowledge, plan recognition-inferring the most likely strategy the student is using to solve a problem-

Associated of Statin Use with Storage Lower Urinary Tract Symptoms: Data Mining of Claims Database
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Abstract Background: It remains uncertain whether or not statin use is associated with development of micturition disorders. To examine the association between statin use and the risk of storage lower urinary tract symptoms (LUTS), data mining was performed on a

Fast and robust Hybrid Particle Swarm Optimization Tabu Search Association Rule Mining (HPSO-ARM) algorithm for Web Data Association Rule Mining (
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Data mining is the process of fetching the desired information from large databases. Extracted information is used for different There exist diverse models of data mining such as classification, clustering, decision tree and neural networks from which

Composite Kernel Machines on Kernel Locally Consistent Concept Factorization Space forData Mining
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ABSTRACT This paper proposes a novel approach to overcome the main problems in high- dimensional data mining. We construct a composite kernel machine (CKM) on a special space (the kernel locally consistent concept factorization (KLCCF) space) to solve three

Community Users in Academic Libraries: Data-Mining for Fund-Raising.
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ABSTRACT The purpose of this paper is to report the third phase of a study on the impact of one North American academic library's extending library privileges gratis to community users. The paper reports results of an appeals letter sent to community users at the

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Abstract Web has become the primary means for information distribution. It is being used for commercial, entertainment or educational purposes and thus its popularity resulted in heavy traffic in the Internet. Web Intelligence (WI) deals with the scientific exploration of the new

Analysis of factors that affect the students academic performance-Data Mining Approach
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ABSTRACT the analysis of students' feedback can reveal imperfections and shortcomings of educational environments. The Common methods of analysis and data evaluation can't singly uncover valuable information that is hidden behind the students' feedback. This

Do Data Mining Methods Support the Three-Group Diagnostic Model of Primary Progressive Aphasia
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Abstract Primary Progressive Aphasia (PPA) is a neurodegenerative disease characterized by a gradual dissolution of language abilities, with higher risk to evolve to dementia. For that reason, discovering the different subtypes of PPA patients is

Analyzing Computer Programming Job Trend Using Web Data Mining
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Abstract Today's rapid changing and competitive environment requires educators to stay abreast of the job market in order to prepare their students for the jobs being demanded. This is more relevant about Information Technology (IT) jobs than others. However, to stay

Performance Analysis of Different Data mining Techniques over Heart Disease dataset
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Abstract Data Mining is an analytic process designed to explore data (usually large amounts of data-typically business or market related-also known as" big data") in search of consistent patterns and/or systematic relationships between variables, and then to validate the

How Data Mining Techniques Can Improve Simulation Studies.
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ABSTRACT Researchers take years and even decades of observation in order to analyze socio-economic phenomenon. Whereas the agent-based modeling simulation (ABMS) provides a new issue by offering the possibility to create virtual societies in which

Validating Predictive Performance of Classifier Models for Multiclass Problem in EducationalData Mining
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Abstract Classification is one of the most frequently studied problems in data mining and machine learning research areas. It consists of predicting the value of a class attribute based on the values of other attributes. There are different classifications models were proposed

Effect of WEKA Filters on the Performance of the NavieBayes Data Mining Algorithm on Arrhythmia and Parkinson's Datasets
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ABSTRACT Data mining is the process of selecting, exploring and modeling a large database in order to discover model and pattern that are unknown. Enormous gathered data in Health care

Application of graph based data mining techniques in administrational systems of education
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Although we use different algorithms to handle with the different representations, we try to analyze the results simultaneously to

Data Mining Vs Statistical Techniques for Classification of NSL-KDD Intrusion Data
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ABSTRACT Intrusion is a kind of malicious attack and is very harmful for individual or for any organization. Due to rapid growing of internet users it has become an important research area Information and network security is becoming an important issue for any organization

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ABSTRACT Companies have been collecting data for decades, building massive data warehouses in which to store it. Even though this data is available, very few companies have been able to realize the actual value stored in it. The question these companies are

Single Level Drill Down Interactive Visualization Technique for Descriptive Data MiningResults.
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Abstract Information technology plays vital role to enhance our knowledge and improve social life. Information presentation is as important as information itself, and interaction with these information enable one to understand these information quickly and easily. In this

A Study of Data Perturbation Techniques For Privacy Preserving Data Mining
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Abstract In recent years, the data mining techniques have met a serious challenge due to the increased concerning and worries of the privacy, that is, protecting the privacy of the critical and sensitive data. Data perturbation is a popular technique for privacy preserving

Semi-Automated Disaggregation of a Conventional Soil Map Using Knowledge Driven Data Miningand Random Forests in the Sonoran Desert, USA
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Conventional soil maps (CSM) have provided baseline soil information for land use planning for over 100 years. Although CSM have been widely used, they are not suitable to meet growing demands for high resolution soil information at field scales. We present a

Data Mining in Telecommunication: A Review
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ABSTRACT Telecommunication is one of the first industries to affect data mining technology. These companies have different types of customer are there. When companies save this data and companies have huge amount of data. In which different types of data are

Encoding of Sensing Data for Effective Data Stream Mining
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Therefore, it was found that the proposed scheme reached the point of reasoning a situation which will be detected and recognized through sensing of multiple sensors. Keywords : Stream Data mining, Sensor data processing, Context inference 1. Introduction

A Study Of Privacy Preserving Data Mining Techniques
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Abstract Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. So people have become increasingly unwilling to share their data, often resulting in individuals either refusing to

Fast and Robust Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Web Data Association Rule Mining
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For this particular reason data mining is attracted by information business and the world and is required to turn data into useful information and knowledge. Data mining is the process of fetching the desired information from large databases.

Data Mining Techniques: To Predict and Resolve Breast Cancer Survivability
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ABSTRACT Breast cancer is one of the deadliest disease, is the most common of all cancers and is the leading cause of cancer deaths in women worldwide, accounting for gt; 1.6% of deaths and case fatality rates are highest in low-resource countries. The breast cancer

A Review on the Role of Domain Driven Data Mining
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ABSTRACT Knowledge Discovery and Data Mining (KDD) refer to the overall process of discovering useful knowledge from data. It involves evaluation and possibly interpretation of the patterns to make decision of what qualifies as knowledge and gives choice of

A Novel Data Mining based Hybrid Intrusion Detection Framework
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Abstract. The prosperity of technology worldwide has made the concerns of security tend to increase rapidly. The enormous usage of internetworking has raised the need of protecting system (s) as well as network (s) from the unauthorized access (intrusion). To tackle the

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ABSTRACT Hepatitis virus infection substantially increases the risk of chronic liver disease and hepatocellular carcinoma in humans and also affects majority of population in all age groups. It is the major challenge for many hospitals and public health care services for

A Comparative Study of Different Data Mining Algorithms
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Abstract Data Mining is used extensively in many sectors today, viz., business, health, security, informatics etc. The successful application of data mining algorithms can be seen in marketing, retail, and other sectors of the industry. The aim of this paper is to present the

A data mining approach for identifying novel target specific small molecules
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Background There has been a paradigm shift in drug discovery from being a single-target approach to a multi-target comparative analysis. This has shifted the emphasis from designing lead candidates with desirable pharmacokinetic properties against individual

TCM Data Mining and Quality Evaluation with SAPHRON TM System
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Abstract Traditional Chinese Medicine (TCM) is an invaluable human heritage for its documented clinical experience of thousands of years. However, it remains problematic for how to make the best use of this treasure. A major concern of TCM modernization is the

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ABSTRACT This paper talks about the conceivable outcomes of interfacing the fields of computational intelligence (CI), information mining and learning revelation. In this paper delicate registering based information mining calculations are characterized and the

Outlier Mining for Removing the Anomalies in High Dimensional Data Using ARVDH Algorithm
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ABSTRACT In Data mining outliers are one of the main threats for efficient information retrieval from databases. Outliers are also known as Anomalies. Mining of outliers from the normal data is very important and scope of this is very high.

Data Mining using Python exercises for introduction
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Data Mining using Python exercises for introduction Finn Årup Nielsen DTU Compute str and int Write a function, is_hashad that determines whether a number is a Har-

Data Mining Algorithms for Prediction of Soil Organic Matter and Clay Based on Vis-NIR Spectroscopy
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Abstract Organic matter (OM) amount and clay content in the soil are important constituents in the sustainability of agricultural systems. The methods used for OM and clay analyses in laboratories are laborious, time consuming and use require reagents that pollute the

Implementation of an Improved ID3 Decision Tree Algorithm in Data Mining System
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ABSTRACT Inductive learning is the learning that is based on induction. In inductive learning Decision tree algorithms are very famous. For the appropriate classification of the objects with the given attributes inductive methods use these algorithms basically. Decision tree is

Methodology for Selection of a Data Mining Tool
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Abstract In this paper, we describe the procedure for selection of a data mining tool for a software project. These days it is impossible or very difficult for any software developer to ignore the usage of data mining tools. Even some developers are using data mining data

Microarray Gene Expression Data Mining using High End Clustering Algorithm based on Attraction-Repulsion Technique.
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ABSTRACT Microarray Gene expression data analysis is one of the key domains in the modern cellular and molecular biology system design and analysis; shortly we called it computational simulation of genome-wide expression from DNA hybridization. We present

An approach for load balancing for simulation in heterogeneous distributed systems using simulation data mining
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Abstract This paper describes an approach to reduce the computation time of finite element simulations on heterogeneous distributed systems. This should be achieved by enhanced load balancing with help of machine learning techniques. Based on the

A Novel Approach for Breast Cancer Detection using Data Mining Techniques
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ABSTRACT Breast cancer is one of the leading cancers for women when compared to all other cancers. It is the second most common cause of cancer death in women. Breast cancer risk in India revealed that 1 in 28 women develop breast cancer during her lifetime. This is

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ABSTRACT In a data distribution scenario the sensitive data given to agents can be leaked in some cases and can be found in unauthorized places. Our aim is to detect when the distributor's sensitive data have been leaked by agents and to identify the agent who

Comparison of Classifiers in Data Mining
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ABSTRACT Hepatitis virus infection substantially increases the risk of chronic liver disease and hepatocellular carcinoma in humans and also affects majority of population in all age groups. It is the major challenge for many hospitals and public health care services for

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Abstract Most of researches on mining high utility itemsets focus on the static transaction database, where all transactions are treated with the same importance and the database can be scanned more than once. With the emergence of new applications, data stream

Assessing Indian Industries on the Basis of Financial Ratios Using Certain Data Mining Tools
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ABSTRACT Analyzing financial performance of companies and grading them in today's information-rich society can be a daunting task. With the evolution of the technology, Internet access to massive amounts of financial data, typically in the form of financial statements, is

The Research of Data Mining Based on Application Data Pool
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Abstract Today, people use various kinds of information technology applications to deal with applications in our daily life, which generates lots of information. However, most of the informationis just stored in many Distributed Heterogeneous Databases (DHDs) as log

Data Mining for Improving Health-Care Resource Deployment
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While the health care industry accounts for a significant large portion of the GDP, the health care system in the US are still relatively inefficient. Before cutting down unnecessary health care expenses, it is important to ensure that individuals who really need medical attention

A Survey on Wireless Intrusion Detection using Data Mining Techniques
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ABSTRACT An Intrusion Detection System (IDS) is a system for detecting intrusions and reporting to the authority or to the network administration. In recent years, since the computer network keeps expanding drastically, the incidents of data theft and hacking are also

A novel data mining algorithm for mathematics teaching evaluation.
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Abstract With relative theory about technology of data mining and recommender model of user's interest, this paper presents the method of MWFP-TREE based on the combination between recommender model idea of user's weight and the minimum weighted FP-TREE

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Abstract This paper proposes a method comprising procedures to calibrate an EnergyPlus whole building energy model. An occupant behavior data mining procedure is developed and tested in an office building. Workday occupancy schedules are generated

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Abstract This paper aims to predict quality features of Iberian hams by using non- destructive methods of analysis and data mining. Iberian hams were analyzed by Magnetic Resonance Imaging (MRI) and Computer Vision Techniques (CVT) throughout their

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ABSTRACT In today scenario, Data Mining plays a very important role in order to obtain the useful patterns from data sources, eg texts, image, etc. and the pattern needs to be valid. The process of separating previously unknown, accessible and actionable information

Framework for Interactive Data Mining Results Visualization on Mobile Devices.
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Abstract The rapidly improving technologies like data mining and mobile technology need careful investigation in order to emerge these technologies. In this paper we identified the challenges confront by mobile data mining, visualization challenges, and mobile device

Implementation of WSN Based Air Pollution Monitoring System using Data Mining Technique
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ABSTRACT In the Industrial environment there are various technical parameters which have to be maintained. If it is not maintained in the range then it will lead a large catastrophe. So, we need to maintain the climate. There is some parameter which is important like temperature

Multi-Relational secured in Data Mining
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ABSTRACT Multi-Relational Data Mining (MRDM). Building on relational database theory is an obvious choice, as most data-intensive applications of industrial scale employ a relational database for storage and retrieval. But apart from this pragmatic motivation,

Association Rule Mining in Discovering Travel Pattern in Passport Data Analysis
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Data mining is the exploration and analysis of large quantities of data in order to discover valid, novel, potentially useful, and ultimately understandable patterns in data . Data mining is a process that uses a variety of data analysis tools to discover patterns ..

Fault prediction of fan bearing using time series data mining.
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Abstract The fault symptoms are regarded as a sort of temporal patterns hidden in a time series. A novel method based on time series data mining is proposed for the prediction of fan bearing fault. The time series, which is formed by large numbers of fan bearing

Secure Publishing of Ecg Time Series for Privacy-preserving Data Mining
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Abstract We address the secure publishing of ECG time series to preserve mining accuracy as well as privacy. For example, people with heart disease do not want to disclose their ECG time series, but they still allow to mine some accurate patterns from their time series. We

Interactive data mining framework for Chinese traditional therapeutic evaluation.
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Abstract Data mining, which aims at extracting interesting information from large collections of data, has been widely used as an effective decision making tool. This paper broke through traditional therapeutic evaluation method with only by individual experience

Segmentation of Mobile Customers for Improving Profitability Using Data Mining Techniques
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ABSTRACT This work helps in identifying the activities Segmentation of mobile customers of different groups will be done based on some rules. Customers are segregated into groups under the categorization of network providers. There are different types of networks, but

Disease Predication of Cardio-Vascular Diseases, Diabetes and Malignancy in Lungs Based on Data Mining Classification Techniques
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ABSTRACT Data mining technology provides a user oriented approach to extract the hidden information from the large database. There are different algorithms used in data mining techniques like

Predicting Students Performance Using Data Mining Technique with Rough Set Theory Concepts
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ABSTRACT Data being generated in the academic domain and educational perspective are increasing in an exponential rate. There exists many data some are relevant and some are irrelevant. The knowledge extraction from these data will yield wanted and unwanted

Emerging Trends in Associative Classification Data Mining
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ABSTRACT Utilising association rule discovery to learn classifiers in data mining is known as Associative Classification (AC). In the last decade, AC algorithms proved to be effective in devising high accurate classification systems from various types of supervised data sets.

Data Mining Techniques
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ABSTRACT Knowledge discovery in databases is a rapidly growing field, whose development is driven by strong research interests as well as urgent practical, social, and economical needs. In this paper, we provide an overview of common knowledge discovery tasks and

A Survey on Enhancing Data Processing of Positive and Negative Association Rule Mining
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ABSTRACT Importance of data mining has been increased rapidly for business domains like marketing, financing and telecommunications. Keywords/Index Term Data Mining, Data Processing, Outsource Services, Market basket analysis, Ajax Technique.

Hypertension Interventions using Classification Based Data Mining
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ABSTRACT In the present study, we would like to gain the insight of the medical data through classification based data mining technique. The data sets of NCD (Non Communicable Diseases) risk factors, a standard report of Saudi Arabia 2005, in collaboration with WHO (

Data Mining Approach For Subscription-Fraud Detection in Telecommunication Sector
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Abstract This paper implements a probability based method for fraud detection in telecommunication sector. We used Naïve-Bayesian classification to calculate the probability and an adapted version of KL-divergence to identify the fraudulent customers

Research and Design of Web Data Mining
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ABSTRACT As e-business is widely applied, web data mining technology is used for e- business to provide personalized ebusiness and better meet the requirements of users. Beginning from the concept of personalized information services, this paper focuses on

A Survey on Crime Data Analysis of Data Mining Using Clustering Techniques
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ABSTRACT Data mining is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data and it is the process of analyzing data from different perspectives and summarizing it into useful information. Data

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Abstract In this study, eight major factors playing significant role in the Pima Indian population are analyzed. Real time data is taken from the large dataset of National Institute of Diabetes and Digestive and Kidney Diseases. The data is subjected to an analysis by

Systematic Approach for Digital Marketing Strategy through Data Mining Technology
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Abstract. Marketing does not involve only sales and advertising; rather, it helps the manager make good decisions pertaining to business, products, and services. Therefore, from the viewpoint of the business, how to meet market requirement has been an important issue.

Research on Data Mining Technologies for Complicated Attributes Relationship in Digital Library Collections
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ABSTRACT We present here the research work on data mining technologies for complicated attributes relationship in digital library collections. Firstly our work and ideology is introduced as the research background of this paper. Digital library evaluation is an important topic in

Practice-based evidence in medicine: Where information retrieval meets data mining
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A new approach in medical practice is emerging thanks to the increasing availability of large- scale clinical data in electronic form. In practice-based evidence [5, 6], the clinical record is mined to identify patterns of health characteristics, such as diseases that co-occur, side- A new algorithm for fast mining frequent itemsets using N-lists

A new algorithm for fast mining frequent itemsets using N-lists
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ABSTRACT Mining frequent itemsets has emerged as a fundamental problem in data mining and plays an essential role in many important data mining tasks. In this paper, we propose a novel vertical data representation called N-list, which originates from an FP-tree-like

Selected Papers on Analysis of Algorithms

by Donald E. Knuth (Stanford, California: Center for the Study of Language and Information, 2000), xvi+621 pp.
(CSLI Lecture Notes, no. 102.)
ISBN 1-57586-212-3
Printings made after 2006 have xvi+622 pp., because the index has gotten longer.

This is the fourth in a series of eight volumes that contain archival forms of my published papers, together with new material. (The first book in the series was Literate Programming; the second was Selected Papers on Computer Science; the third was Digital Typography.) The Analysis of Algorithms volume is characterized by the following remarks quoted from its preface.

``People who analyze algorithms have double happiness. First of all they experience the sheer beauty of elegant mathematical patterns that surround elegant computational procedures. Then they receive a practical payoff when their theories make it possible to get other jobs done more quickly and more economically.''
I once had the pleasure of writing those words for the Foreword of An Introduction to the Analysis of Algorithms by Robert Sedgewick and Philippe Flajolet, and I can't think of any better way to introduce the present book. After enjoying such double happiness for nearly forty years, I'm delighted that I can finally bring together this collection of essays about the subject that I love most.
... Most of the chapters in this book appeared originally as research papers that solved basic problems related to some particular algorithm or class of algorithms. But the emphasis throughout is on techniques that are of general interest, techniques that should lead also to the solution of tomorrow's problems. The way a problem is solved is generally much more important than the solution itself, and I have therefore tried to explain the principles of solution and discovery as well as I could. Thus I believe the material in this book remains highly relevant even though much of it was written many years ago. I have also appended additional material to most of the chapters, explaining subsequent developments and giving pointers to more recent literature.
... The process of compiling this book has given me an incentive to improve some of the original wording, to make all of the notations consistent with The Art of Computer Programming, to doublecheck almost all of the mathematical formulas and the reasoning that supports them, to correct all known errors, to improve the original illustrations by redrawing them with MetaPost, and to match the bibliographic information with original sources in the library. Thus the articles now appear in a form that I hope will remain useful for at least another generation or two of scholars who will carry the work forward.

This book isn't exactly ``Analysis of Algorithms for Dummies,'' but it does contain expositions of nearly every important aspect of the subject. It has the following chapters:

  1. Mathematical Analysis of Algorithms [P46]
  2. The Dangers of Computer Science Theory [P56]
  3. The Analysis of Algorithms [P44]
  4. Big Omicron and Big Omega and Big Theta [Q43]
  5. Optimal Measurement Points for Program Frequency Counts [P60]
  6. Estimating the Efficiency of Backtrack Programs [P69]
  7. Ordered Hash Tables [P64]
  8. Activity in an Interleaved Memory [P74]
  9. An Analysis of Alpha-Beta Pruning [P70]
  10. Notes on Generalized Dedekind Sums [P75]
  11. The Distribution of Continued Fraction Approximations [P106]
  12. Evaluation of Porter's Constant [P86]
  13. Analysis of the Subtractive Algorithm for Greatest Common Divisors [P76]
  14. Length of Strings for a Merge Sort [P12]
  15. The Average Height of Planted Plane Trees [P51]
  16. The Toilet Paper Problem [P111]
  17. An Analysis of Optimum Caching [P104]
  18. A Trivial Algorithm Whose Analysis Isn't [P84]
  19. Deletions That Preserve Randomness [P89]
  20. Analysis of a Simple Factorization Algorithm [P78]
  21. The Expected Linearity of a Simple Equivalence Algorithm [P88]
  22. Textbook Examples of Recursion [P135]
  23. An Exact Analysis of Stable Allocation [P149]
  24. Stable Husbands [P127]
  25. Shellsort With Three Increments [P157]
  26. The Average Time for Carry Propagation [P90]
  27. Linear Probing and Graphs [P158]
  28. A Terminological Proposal [Q33]
  29. Postscript About NP-hard Problems [Q36]
  30. An Experiment in Optimal Sorting [P52]
  31. Duality in Addition Chains [Q58]
  32. Complexity Results for Bandwidth Minimization [P77]
  33. The Problem of Compatible Representatives [P132]
  34. The Complexity of Nonuniform Random Number Generation [P80]

(Numbers like P158 and Q33 in this list refer to the corresponding papers in my list of publications.)

This book can be ordered from the publisher (CSLI), and also from the distributor (University of Chicago Press).

The papers in this book are a collection of gems that were previously published or presented as lectures by the author. The very title bears Knuth's signature, since it was he who introduced the phrase ``analysis of algorithms.'' ... He presents new ``pure'' results, which is surprising in view of the age of the topics. Generally, nobody will have the last word, as most of the papers include a sizable supplement discussing more recent developments. ... This collection will be highly prized by Knuth fans---in fact, by all computer scientists.
--Harvey Cohn, in Computing Reviews (July 2000)
The range of topics, although mostly confined to the analysis of algorithms, is vast. ... To focus on any one aspect ... would give short-shrift to the others. I therefore decided to focus this review on why I believe every reader of SIGACT News should buy this book. ... The evolution of current technology and fundamental unifying ideas can be found ... excellent reading for a beginning student in algorithm analysis ... many hilarious misapplications of theory to practice ... Knuth does an amazing job of relating how he approaches problems rather than merely recording a highly polished solution which to the reader seems to come out of the blue ... It's just plain fun to read.
--Timothy H. McNicholl, in SIGACT News (March 2001)
... combines succinctness and formal rigour with clarity and humour ... These papers on algorithmic analysis exemplify how best to convey the often complex mathematics lying behind the behaviour of apparently simple techniques. ... this collection will be of interest to those who value the highest quality technical writing, as well as to algorithm analyzers.
--Greg Michaelson, in The Computer Bulletin (May 2001)
... In summary, the papers collected here give a beautiful picture of charms and challenges of the (average-case) analysis of algorithms by the pen of its creator.
--Joachim von zur Gathen, in IEEE Annals of the History of Computing (April--June 2002)
... The particular value of this book is that much of the material has appeared in publications which are available only with difficulty. The collection is a valuable addition to the literature.
--A. D. Booth, in Mathematical Reviews (2001)
... thoughtful, thorough, and inspiring. ... Most of these papers, although excellent in every sense of this word, are quite technical, and I certainly don't recommend them for ``easy reading.'' ... I would whole-heartedly recommend this book to my colleagues who have some time to spare, and who would like to get into one of the greatest minds in computer science.
--Zhizhang Shen, in Zentralblatt Math (March 2001)


As usual, I promise to pay a reward of $2.56 to the first person who finds and reports anything that remains technically, historically, typographically, or politically incorrect.

The printing of 2012 corrected all of the previously known errors in the original printing of 2000 and errors in the printing of 2008; the following further corrections are still needed. An asterisk (*) marks technical errors that are not merely typographical:

*page 64, line 5 from the bottom
change 'nonnegative' to 'positive'
*page 65, line 11
change 'perfect:' to 'perfect, if we assume that all costs at terminal nodes are positive:'
page 233, in reference [1]
change 'considerations geometriques' to 'considérations géométriques'
page 472, new copy
Nicholas Pippenger [“Analysis of carry propagation in addition: An elementary approach,” Journal of Algorithms42 (2002), 317--333] has explained how to obtain these results without using contour integration.
page 603, line 7
change 'cofficients' to 'coefficients'
page 626, right column, in the entry for Nicholas John Pippenger
change '501' to '472, 501'

I hope the book is otherwise error-free; but (sigh) it probably isn't, because each page presented me with hundreds of opportunities to make mistakes. Please send suggested corrections to , or send snail mail to Prof. D. Knuth, Computer Science Department, Gates Building 4B, Stanford University, Stanford, CA 94305-9045 USA. In either case please include your postal address, so that I can mail an official certificate of deposit as a token of thanks for any improvements to which you have contributed.

I may not be able to read your message until many months have gone by, because I'm working intensively on The Art of Computer Programming. However, I promise to reply in due time.

DO NOT SEND EMAIL TO KNUTH-BUG EXCEPT TO REPORT ERRORS IN BOOKS! And if you do report an error via email, please do not include attachments of any kind; your message should be readable on brand-X operating systems for all values of X.

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