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Data Mining:Concepts, Models, Methods, and Algorithms

Cover Image Copyright Year: 2011
Author(s): Kantardzic, M.
Publisher: Wiley-IEEE Press
Content Type : Books & eBooks
Topics: Communication, Networking & Broadcasting ;  Computing & Processing (Hardware/Software)
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Abstract

Now updated—the systematic introductory guide to modern analysis of large data setsAs data sets continue to grow in size and complexity, there has been an inevitable move towards indirect, automatic, and intelligent data analysis in which the analyst works via more complex and sophisticated software tools. This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces to extract new information for decision-making.This Second Edition of Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples, and questions and exercises for practice at the end of each chapter. This new edition features the following new techniques/methodologies:
Support Vector Machines (SVM)—developed based on statistical learning theory, they have a large potential for applications in predictive data mining
Kohonen Maps (Self-Organizing Maps - SOM)—one of very applicative neural-networks-based methodologies for descriptive data mining and multi-dimensional data visualizations
DBSCAN, BIRCH, and distributed DBSCAN clustering algorithms—representatives of an important class of density-based clustering methodologies
Bayesian Networks (BN) methodology often used for causality modeling
Algorithms for measuring Betweeness and Centrality parameters in graphs, important for applications in mining large social networks
CART algorithm and Gini index in building decision trees
Bagging & Boosting approaches to ensemble-learning methodologies, with details of AdaBoost algorithm
Relief algorithm, one of the core feature selection algorithms inspired by instance-based learning
PageRank algorithm for mining and authority ranking of web pages
Latent Semantic Analysis (LSA) for text mining and measuring semantic similarities between text-based documents
New sections on temporal, spatial, web, text, parallel, and distributed data mining
More emphasis on business, privacy, security, and legal aspects of data mining technologyThis text offers guidance on how and when to use a particular software tool (with the companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided. The book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here.This volume is primarily intended as a data-mining textbook for computer science, computer engineering, and computer information systems majors at the graduate level. Senior students at the undergraduate level and with the appropriate background can also successfully comprehend all topics presented here.

  •   Click to expandTable of Contents

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      Frontmatter

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.fmatter
      Page(s): i - xvii
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      The prelims comprise:
      Half Title
      IEEE Press Editorial Board
      Title
      Copyright
      Dedication
      Contents
      Preface to the Second Edition
      Preface to the First Edition View full abstract»

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      Data-Mining Concepts

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.ch1
      Page(s): 1 - 25
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This chapter contains sections titled:
      Introduction
      Data-Mining Roots
      Data-Mining Process
      Large Data Sets
      Data Warehouses for Data Mining
      Business Aspects of Data Mining: Why a Data-Mining Project Fails
      Organization of This Book
      Review Questions and Problems
      References for Further Study View full abstract»

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      Preparing the Data

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.ch2
      Page(s): 26 - 52
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This chapter contains sections titled:
      Representation of Raw Data
      Characteristics of Raw Data
      Transformation of Raw Data
      Missing Data
      Time-Dependent Data
      Outlier Analysis
      Review Questions and Problems
      References for Further Study View full abstract»

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      Data Reduction

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.ch3
      Page(s): 53 - 86
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This chapter contains sections titled:
      Dimensions of Large Data Sets
      Feature Reduction
      Relief Algorithm
      Entropy Measure for Ranking Features
      PCA
      Value Reduction
      Feature Discretization: ChiMerge Technique
      Case Reduction
      Review Questions and Problems
      References for Further Study View full abstract»

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      Learning from Data

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.ch4
      Page(s): 87 - 139
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This chapter contains sections titled:
      Learning Machine
      SLT
      Types of Learning Methods
      Common Learning Tasks
      SVMs
      kNN: Nearest Neighbor Classifier
      Model Selection versus Generalization
      Model Estimation
      90% Accuracy: Now What?
      Review Questions and Problems
      References for Further Study View full abstract»

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      Statistical Methods

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.ch5
      Page(s): 140 - 168
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This chapter contains sections titled:
      Statistical Inference
      Assessing Differences in Data Sets
      Bayesian Inference
      Predictive Regression
      ANOVA
      Logistic Regression
      Log-Linear Models
      LDA
      Review Questions and Problems
      References for Further Study View full abstract»

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      Decision Trees and Decision Rules

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.ch6
      Page(s): 169 - 198
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This chapter contains sections titled:
      Decision Trees
      C4.5 Algorithm: Generating a Decision Tree
      Unknown Attribute Values
      Pruning Decision Trees
      C4.5 Algorithm: Generating Decision Rules
      CART Algorithm & Gini Index
      Limitations of Decision Trees and Decision Rules
      Review Questions and Problems
      References for Further Study View full abstract»

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      Artificial Neural Networks

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.ch7
      Page(s): 199 - 234
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This chapter contains sections titled:
      Model of an Artificial Neuron
      Architectures of ANNs
      Learning Process
      Learning Tasks Using ANNs
      Multilayer Perceptrons (MLPs)
      Competitive Networks and Competitive Learning
      SOMs
      Review Questions and Problems
      References for Further Study View full abstract»

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      Ensemble Learning

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.ch8
      Page(s): 235 - 248
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This chapter contains sections titled:
      Ensemble-Learning Methodologies
      Combination Schemes for Multiple Learners
      Bagging and Boosting
      AdaBoost
      Review Questions and Problems
      References for Further Study View full abstract»

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      Cluster Analysis

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.ch9
      Page(s): 249 - 279
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This chapter contains sections titled:
      Clustering Concepts
      Similarity Measures
      Agglomerative Hierarchical Clustering
      Partitional Clustering
      Incremental Clustering
      DBSCAN Algorithm
      BIRCH Algorithm
      Clustering Validation
      Review Questions and Problems
      References for Further Study View full abstract»

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      Association Rules

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.ch10
      Page(s): 280 - 299
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This chapter contains sections titled:
      Market-Basket Analysis
      Algorithm Apriori
      From Frequent Itemsets to Association Rules
      Improving the Efficiency of the Apriori Algorithm
      FP Growth Method
      Associative-Classification Method
      Multidimensional Association-Rules Mining
      Review Questions and Problems
      References for Further Study View full abstract»

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      Web Mining and Text Mining

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.ch11
      Page(s): 300 - 327
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This chapter contains sections titled:
      Web Mining
      Web Content, Structure, and Usage Mining
      HITS and LOGSOM Algorithms
      Mining Path-Traversal Patterns
      PageRank Algorithm
      Text Mining
      Latent Semantic Analysis (LSA)
      Review Questions and Problems
      References for Further Study View full abstract»

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      Advances in Data Mining

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.ch12
      Page(s): 328 - 384
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This chapter contains sections titled:
      Graph Mining
      Temporal Data Mining
      Spatial Data Mining (SDM)
      Distributed Data Mining (DDM)
      Correlation Does Not Imply Causality
      Privacy, Security, and Legal Aspects of Data Mining
      Review Questions and Problems
      References for Further Study View full abstract»

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      Genetic Algorithms

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.ch13
      Page(s): 385 - 413
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This chapter contains sections titled:
      Fundamentals of GAs
      Optimization Using GAs
      A Simple Illustration of a GA
      Schemata
      TSP
      Machine Learning Using GAs
      GAs for Clustering
      Review Questions and Problems
      References for Further Study View full abstract»

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      Fuzzy sets and Fuzzy Logic

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.ch14
      Page(s): 414 - 446
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This chapter contains sections titled:
      Fuzzy Sets
      Fuzzy-Set Operations
      Extension Principle and Fuzzy Relations
      Fuzzy Logic and Fuzzy Inference Systems
      Multifactorial Evaluation
      Extracting Fuzzy Models from Data
      Data Mining and Fuzzy Sets
      Review Questions and Problems
      References for Further Study View full abstract»

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      Visualization Methods

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.ch15
      Page(s): 447 - 469
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This chapter contains sections titled:
      Perception and Visualization
      Scientific Visualization and Information Visualization
      Parallel Coordinates
      Radial Visualization
      Visualization Using Self-Organizing Maps (SOMs)
      Visualization Systems for Data Mining
      Review Questions and Problems
      References for Further Study View full abstract»

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      Appendix A

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.app1
      Page(s): 470 - 495
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This appendix contains sections titled:
      Data-Mining Journals
      Data-Mining Conferences
      Data-Mining Forums/Blogs
      Data Sets
      Comercially and Publicly Available Tools
      Web Site Links View full abstract»

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      Appendix B: Data-Mining Applications

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.app2
      Page(s): 496 - 509
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      This appendix contains sections titled:
      Data Mining for Financial Data Analysis
      Data Mining for the Telecomunications Industry
      Data Mining for the Retail Industry
      Data Mining in Health Care and Biomedical Research
      Data Mining in Science and Engineering
      Pitfalls of Data Mining View full abstract»

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      Bibliography

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.biblio
      Page(s): 510 - 528
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      No abstract. View full abstract»

    • Full text access may be available. Click article title to sign in or learn about subscription options.

      Index

      Kantardzic, M.
      Data Mining:Concepts, Models, Methods, and Algorithms

      DOI: 10.1002/9781118029145.index
      Page(s): 529 - 534
      Copyright Year: 2011

      Wiley-IEEE Press eBook Chapters

      No abstract. View full abstract»




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