Felici G., Vercellis C. Mathematical Methods for Knowledge Discovery and Data Mining

IGI, 2008. — 394 p. — ISBN13: 9781599045283; ISBN10: 1599045281; EISBN13 9781599045306.The field of data mining has seen a demand in recent years for the development of ideas and results in an integrated structure.
Mathematical Methods for Knowledge Discovery & Data Mining 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; and many others. This Premier Reference Source is an invaluable resource for scholars and practitioners in the fields of biomedicine, engineering, finance and insurance, manufacturing, marketing, performance measurement, and telecommunications.Topics Covered:The many academic areas covered in this publication include, but are not limited to:
Analysis of service quality
Bayesian belief networks
Control signatures
Data cleaning
Data mining and visualization techniques
Discretization of rational data
Distributed knowledge discovery
Evolutionary Algorithms
Fuzzy miner
Genetic clustering
Genmax algorithms
Hierarchical clustering
Hierarchical profiling
Hybrid data mining
Kernel width selection
Logical commonsense reasoning operations
Machine learning algorithms
Markov chains models
Multicategory discrete SVM
Probabilistic principal surfaces
Protein folding classification
Routing attribute data mining
Rule-based classification
Spatial navigation assistance system
Stated preference models
Support vector machines
SVM classification
Time series data mining
Vector DNF for datasets classification
Web clickstream analysis