Stream Data Mining: Algorithms and Their Probabilistic Properties

Stream Data Mining: Algorithms and Their Probabilistic Properties
  • eBook:
    Stream Data Mining: Algorithms and Their Probabilistic Properties
  • Author:
    Leszek Rutkowski, Maciej Jaworski, Piotr Duda
  • Edition:
  • Categories:
  • Data:
    May 17, 2020
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    340 pages
  • Format:
    PDF, ePUB

Book Description
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.


1. Introduction and Overview of the Main Results of the Book

Part I - Data Stream Mining
2. Basic Concepts of Data Stream Mining

Part II - Splitting Criteria in Decision Trees for Data Stream Mining
3. Decision Trees in Data Stream Mining
4. Splitting Criteria Based on the McDiarmid’s Theorem
5. Misclassification Error Impurity Measure
6. Splitting Criteria with the Bias Term
7. Hybrid Splitting Criteria

Part III - Probabilistic Neural Networks for Data Stream Mining
8. Basic Concepts of Probabilistic Neural Networks
9. General Non-parametric Learning Procedure for Tracking Concept Drift
10. Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks
11. Probabilistic Neural Networks for the Streaming Data Classification

Part IV - Ensemble Methods
12. The General Procedure of Ensembles Construction in Data Stream Scenarios
13. Classification
14. Regression
15. Final Remarks and Challenging Problems

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