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Data Mining: Theories, Algorithms, and Examples

Data Mining: Theories, Algorithms, and Examples

Hot Research Book in Data Mining: Theories, Algorithms, and Examples

Author(s) Name:  Nong Ye

About the Book:

   New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms.
   The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures.
   The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them.

Table of Contents

  • AN OVERVIEW OF DATA MINING METHODOLOGIES
  •   Introduction to data mining methodologies
  • METHODOLOGIES FOR MINING CLASSIFICATION AND PREDICTION PATTERNS
  •   Regression models
      Bayes classifiers
      Decision trees
      Multi-layer feedforward artificial neural networks
      Support vector machines
  • METHODOLOGIES FOR MINING CLUSTERING AND ASSOCIATION PATTERNS
  •   Hierarchical clustering
      Partitional clustering
      Self-organized map
      Probability distribution estimation
      Association rules
      Bayesian networks
  • METHODOLOGIES FOR MINING DATA REDUCTION PATTERNS
  •   Principal components analysis
      Multi-dimensional scaling
      Latent variable analysis
  • METHODOLOGIES FOR MINING OUTLIER AND ANOMALY PATTERNS
  •   Univariate control charts
      Multivariate control charts
  • METHODOLOGIES FOR MINING SEQUENTIAL AND TIME SERIES PATTERNS
  •   Autocorrelation based time series analysis
      Hidden Markov models for sequential pattern mining
      Wavelet analysis
      Hilbert transform
      Nonlinear time series analysis

    ISBN:  9781439808382

    Publisher:  CRC Press

    Year of Publication:  2013

    Book Link:  Home Page Url