Mathematical Methods for Knowledge Discovery and Data Mining

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

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