Mathematical Methods for Knowledge Discovery and Data Mining

Sep 21, 2026

Uploaded by Maeve

Mathematical Methods for Knowledge Discovery and Data Mining presents mathematical models and methods that underpin data mining applications and solution techniques. Coverage includes association rules, Bayesian methods, data visualization, kernel methods, and neural networks, along with text, speech, and image recognition. It serves scholars and practitioners and spans domains such as biomedicine, engineering, finance, manufacturing, marketing, performance measurement, and telecommunications. The book includes bibliographical references and an index.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages394

Reading Time16 h 33 min

AccessFree to download

Foreword

Preface

Acknowledgment

Discretization of Rational Data

Vector DNF for Datasets Classifications: Application to the Financial Timing Decision Problem

Reducing a Class of Machine Learning Algorithms to Logical Commonsense Reasoning Operations

The Analysis of Service Quality Through Stated Preference Models and Rule-Based Classification

Support Vector Machines for Business Applications

Kernel Width Selection for SVM Classification: A Meta-Learning Approach

Protein Folding Classification Through Multicategory Discrete SVM

Hierarchical Profiling, Scoring, and Applications in Bioinformatics

Hierarchical Clustering Using Evolutionary Algorithms

Exploratory Time Series Data Mining by Genetic Clustering

Development of Control Signatures with a Hybrid Data Mining and Genetic Algorithm

Bayesian Belief Networks for Data Cleaning

A Comparison of Revision Schemes for Cleaning Labeling Noise

Improving Web Clickstream Analysis: Markov Chains Models and Genmax Algorithms

Mathematical Methods for Knowledge Discovery and Data Mining Giovanni Felici Consig lio Nazionale delle Ricerche, Rome, Italy Carlo Vercellis Politec nico di Milano, Italy InformatIon scIence reference Hershey • New York Acquisitions Editor: Kristin Klinger Development Editor: Kristin Roth Senior Managing Editor: Jennifer Neidig Managing Editor: Sara Reed Copy Editor: Angela Thor Typesetter: Jamie Snavely Cover Design: Lisa Tosheff Printed at: Yurchak Printing Inc. Published in the United States of America by Information Science Reference (an imprint ofIGI Global) 701 E. Chocolate Avenue, Suite 200 Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [cust@igi-global.com](cust@igi-global.com) Web site: [http://www.igi-global.com/reference](http://www.igi-global.com/reference) and in the United Kingdom by Information Science Reference (an imprint ofIGI Global) 3 Henrietta Street Covent Garden London WC2E 8LU Tel: 44 20 7240 0856 Fax: 44 20 7379 0609 Web site: [http://www.eurospanonline.com](http://www.eurospanonline.com) Copyright © 2008 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Felici, Giovanni. Mathematical methods for knowledge discovery and data mining / Giovanni Felici & Carlo Vercellis, editors. [p. cm](p. cm). Summary: “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. Includes bibliographical references and index. ISBN 978-1-59904-528-3 (hardcover) --ISBN 978-1-59904-530-6 (ebook) 1. Data mining. 2. Data mining--Mathematical models. 3. Knowledge acquisition (Expert systems) I. Felici, Giovanni. II. Vercellis, Carlo. III. Title. QA76 .9.D343F46 2007 006.3’12--dc22 2007022228 British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book set is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. Table of Contents Foreword ............................................................................................................................................. xii Preface ................................................................................................................................................ xiv Acknowledgment ................................................................................................................................ xx Chapter I Discretization of Rational Data / Jonathan Mugan and Klaus Truemper .............................................. 1 Chapter II Vector DNF for Datasets Classifications: Application to the Financial Timing Decision Problem / Massimo Liquori and Andrea Scozzari ................................................................. 24 Chapter III Reducing a Class of Machine Learning Algorithms to Logical Commonsense Reasoning Operations / Xenia Naidenova ........................................................................................... 41 Chapter IV The Analysis of Service Q

FAQs

What is the book focused on?

It focuses on mathematical models and methods that support data mining applications and solution techniques across many problem domains.

Which data mining topics are covered?

Topics include association rules, Bayesian methods, data visualization, kernel methods, neural networks, and recognition methods for text, speech, and images.

What kinds of references and navigation tools are included?

The book includes bibliographical references and an index, and it provides a detailed table of contents listing the chapters and their topics.

Sep 21, 2026

Uploaded by Maeve