Mathematical Methods for Knowledge Discovery and Data Mining, 1st Edition

  • Published By:
  • ISBN-10: 1599045303
  • ISBN-13: 9781599045306
  • Grade Level Range: College Freshman - College Senior
  • 371 Pages | eBook
  • Original Copyright 2007 | Published/Released December 2007
  • This publication's content originally published in print form: 2007

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This book focuses on the mathematical models and methods that support most data mining applications and solution techniques, covering such topics as association rules; Bayesian methods; data visualization; kernel methods; neural networks; text, speech, and image recognition; an invaluable resource for scholars and practitioners in the fields of biomedicine, engineering, finance, manufacturing, marketing, performance measurement, and telecommunications.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Table of Contents.
Detailed Table of Contents.
1: Discretization of Rational Data.
2: Vector DNF for Datasets Classifications: Application to the Financial Timing Decision Problem.
3: Reducing a Class of Machine Learning Algorithms to Logical Commonsense Reasoning Operations.
4: The Analysis of Service Quality through Stated Preference Models and Rule–Based Classification.
5: Support Vector Machines for Business Applications.
6: Kernel Width Selection for SVM Classification: A Meta–Learning Approach.
7: Protein Folding Classification through Multicategory Discrete SVM.
8: Hierarchical Profiling, Scoring, and Applications in Bioinformatics.
9: Hierarchical Clustering Using Evolutionary Algorithms.
10: Exploratory Time Series Data Mining by Genetic Clustering.
11: Development of Control Signatures with a Hybrid Data Mining and Genetic Algorithm.
12: Bayesian Belief Networks for Data Cleaning.
13: A Comparison of Revision Schemes for Cleaning Labeling Noise.
14: Improving Web Clickstream Analysis: Markov Chains Models and Genmax Algorithms.
15: Advanced Data Mining and Visualization Techniques with Probabilistic Principal Surfaces: Applications to Astronomy and Genetics.
16: Spatial Navigation Assistance System for Large Virtual Environments: The Data Mining Approach.
17: Using Grids for Distributed Knowledge Discovery.
18: Fuzzy Miner: Extracting Fuzzy Rules from Numerical Patterns.
19: Routing Attribute Data Mining Based on Rough Set Theory.
Compilation of References.
About the Contributors.