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Stream Data Mining: Algorithms and Their Probabilistic Properties

  • Leszek Rutkowski
  • Maciej Jaworski
  • Piotr Duda
Book

Part of the Studies in Big Data book series (SBD, volume 56)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Leszek Rutkowski, Maciej Jaworski, Piotr Duda
    Pages 1-10
  3. Data Stream Mining

    1. Front Matter
      Pages 11-11
    2. Leszek Rutkowski, Maciej Jaworski, Piotr Duda
      Pages 13-33
  4. Splitting Criteria in Decision Trees for Data Stream Mining

    1. Front Matter
      Pages 35-35
    2. Leszek Rutkowski, Maciej Jaworski, Piotr Duda
      Pages 37-50
    3. Leszek Rutkowski, Maciej Jaworski, Piotr Duda
      Pages 51-62
    4. Leszek Rutkowski, Maciej Jaworski, Piotr Duda
      Pages 63-82
    5. Leszek Rutkowski, Maciej Jaworski, Piotr Duda
      Pages 83-89
    6. Leszek Rutkowski, Maciej Jaworski, Piotr Duda
      Pages 91-113
  5. Probabilistic Neural Networks for Data Stream Mining

    1. Front Matter
      Pages 115-115
    2. Leszek Rutkowski, Maciej Jaworski, Piotr Duda
      Pages 117-154
    3. Leszek Rutkowski, Maciej Jaworski, Piotr Duda
      Pages 155-172
    4. Leszek Rutkowski, Maciej Jaworski, Piotr Duda
      Pages 245-277
  6. Ensemble Methods

    1. Front Matter
      Pages 279-279
    2. Leszek Rutkowski, Maciej Jaworski, Piotr Duda
      Pages 281-286
    3. Leszek Rutkowski, Maciej Jaworski, Piotr Duda
      Pages 287-308
    4. Leszek Rutkowski, Maciej Jaworski, Piotr Duda
      Pages 309-322
    5. Leszek Rutkowski, Maciej Jaworski, Piotr Duda
      Pages 323-327
  7. Back Matter
    Pages 329-330

About this book

Introduction

This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who deal with stream data, e.g. in telecommunication, banking, and sensor networks.


Keywords

Big Data Data Science Stream Data Mining Streaming Stream Data Algorithms

Authors and affiliations

  • Leszek Rutkowski
    • 1
  • Maciej Jaworski
    • 2
  • Piotr Duda
    • 3
  1. 1.Institute of Computational IntelligenceCzestochowa University of TechnologyCzęstochowaPoland
  2. 2.Information Technology InstituteUniversity of Social SciencesLodzPoland
  3. 3.Institute of Computational IntelligenceCzestochowa University of TechnologyCzęstochowaPoland

Bibliographic information

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