Mathematical Methods for Knowledge Discovery and Data Mining

Giovanni Felici (Consiglio Nazionale delle Richerche, Italy) and Carlo Vercellis (Politecnico di Milano, Italy)

Publication Status: E-Book and Print Version Available for Purchase

ISBN13: 9781599045283

EISBN13: 9781599045306

DOI: 10.4018/978-1-59904-528-3

Description:

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

Coverage:

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

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This book aims to provide a rich collection of current research on a broad array of topics in data mining, ranging from recent theoretical advancements in the field to relevant applica¬tions in diverse domains.

– Giovanni Felici, Consiglio Nazionale delle Ricerche, Italy

Giovanni Felici graduated in Statistics at the University of Rome “La Sapienza”. He received his M.Sc. in Operations Research and Operations Management at the University of Lancaster, UK, in 1990 nd his Ph.D. in Operations Research at the University of Rome “La Sapienza”. He is presently a permanent researcher at IASI, the Istituto di Analisi dei Sistemi ed Informatica of the National Research Council (CNR), where he started his research activity in 1994 working on research projects in logic programming and mathematical optimization. His current research activity is mainly devoted to to the application of optimization techniques to data mining problems, with particular focus on integer programming algorithms for learning in logic and expert systems.

Carlo Vercellis is full professor at the Politecnico di Milano, where he teaches courses in Optimization and Business intelligence. He is also director of the research group MOLD - mathematical modeling, optimization, learning from data. Previously, after his graduation in Mathematics at the Università di Milano, he has been with the National Research Council (CNR), the Bocconi University, the Università di Milano. He has coordinated national and international research programs funded by EEC, CNR and MIUR. His current research interests include mathematical models for learning, such as support vector machines and classification trees; data mining and machine learning, and their applications to relational marketing and biolife sciences; optimization models and methods, in particular with applications to supply chain and revenue management. In the past he was involved in research on design and analysis of algorithms for combinatorial optimization, project management, transportation models. He is author of several books and more than seventy papers, mostly appeared in refereed international journals and edited books.

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