Includes bibliographical references (pages 338-360) and index.
CONTENTS NOTE
Text of Note
Discretization of rational data / Jonathan Mugan, Klaus Truemper -- Vector DNF for datasets classification: application to the financial timing decision problem / Massimo Liquori, Andrea Scozzari -- Reducing a class of machine learning algorithms to logical commonsense reasoning operations / Xenia Naidenova -- The analysis of service quality through stated preference models and rule-based classification / Giovanni Felici, Valerio Gatta -- Support vector machines for business applications / Brian C. Lovell, Christian J. Walder -- Kernel width selection for SVM classification: A meta-learning approach / Shawkat Ali, Kate A. Smith -- Protein folding classification through multicategory discrete SVM / Carlotta Orsenigo, Carlo Vercellis -- Hierarchical profiling, scoring and applications in bioinformatics / Li Liao -- Hierarchical clustering using evolutionary algorithms / Monica Chiş -- Exploratory time series data mining by genetic clustering / T. Warren Liao -- Development of control signatures with a hybrid data mining and genetic algorithm approach Alex Burns, Shital Shah, and Andrew Kusiak -- Bayesian belief networks for data cleaning / Enrico Fagiuoli, Sara Omerino, Fabio Stella -- A comparison of revision schemes for cleaning labeling noise / Chuck P. Lam, David G. Stork -- Improving web clickstream analysis: Markov chains models and Genmax algorithms / Paolo Baldini, Paolo Giudici -- Advanced data mining and visualization techniques with probabilistic principal surfaces: application to astronomy and genetics / Antonino Staiano [and others] -- Spatial navigation assistance system for large virtual environments: the data mining approach / Mehmed Kantardzic, Pedram Sadeghian, Walaa M. Sheta -- Using grids for distributed knowledge discovery / Antonio Congiusta, Domenico Talia, Paolo Trunfio Fuzzy miner: extracting fuzzy rules from numerical patterns / Nikos Pelekis [and others] --Routing attribute data mining based on rough set theory / Yanbing Liu, Menghao Wang, Hong Tang.
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SUMMARY OR ABSTRACT
Text of Note
"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"--Provided by publisher.
OTHER EDITION IN ANOTHER MEDIUM
Title
Mathematical methods for knowledge discovery and data mining.
International Standard Book Number
1599045281
PIECE
Title
Idea Group
TOPICAL NAME USED AS SUBJECT
Data mining-- Mathematical models.
Data mining.
Knowledge acquisition (Expert systems)
Acquisition des connaissances (Systèmes experts)
Exploration de données (Informatique)
Exploration de données (Informatique)-- Modèles mathématiques.