Mathematical Methods for Knowledge Discovery and Data Mining

Mathematical Methods for Knowledge Discovery and Data Mining

Giovanni Felici, Carlo Vercellis

Copyright: © 2008 |Pages: 394

ISBN13: 9781599045283|ISBN10: 1599045281|EISBN13: 9781599045306|ISBN13 Softcover: 9781616926564

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

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Felici, Giovanni, and Carlo Vercellis, editors. Mathematical Methods for Knowledge Discovery and Data Mining. IGI Global, 2008. https://doi.org/10.4018/978-1-59904-528-3

APA

Felici, G. & Vercellis, C. (Eds.). (2008). Mathematical Methods for Knowledge Discovery and Data Mining. IGI Global. https://doi.org/10.4018/978-1-59904-528-3

Chicago

Felici, Giovanni, and Carlo Vercellis, eds. Mathematical Methods for Knowledge Discovery and Data Mining. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-528-3

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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

Table of Contents

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Front Materials

Title Page

Copyright Page

Foreword

Preface

Acknowledgment

Chapters

Chapter 1

Discretization of Rational Data (pages 1-23)

Jonathan Mugan, Klaus Truemper

Frequently, one wants to extend the use of a classification method that, in principle, requires records with True/False values, so that records with rational numbers can be processed. In such cases, the rational numbers must first be...

Chapter 2

Vector DNF for Datasets Classifications: Application to the Financial Timing Decision Problem (pages 24-40)

Massimo Liquori, Andrea Scozzari

Traditional classification approaches consider a dataset formed by an archive of observations classified as positive or negative according to a binary classification rule. In this paper we consider the Financial Timing Decision...

Chapter 3

Reducing a Class of Machine Learning Algorithms to Logical Commonsense Reasoning Operations (pages 41-64)

Xenia Naidenova

The purpose of this paper is to demonstrate the possibility of transforming a large class of machine learning algorithms into commonsense reasoning processes based on using well-known deduction and induction logical rules. The...

Chapter 4

The Analysis of Service Quality Through Stated Preference Models and Rule-Based Classification (pages 65-81)

Giovanni Felici, Valerio Gatta

The analysis of quality of services is an important issue for the planning and the management of many businesses. The ability to address the demands and the relevant needs of the customers of a given service is crucial to determine...

Chapter 5

Support Vector Machines for Business Applications (pages 82-100)

Brian C. Lovell, Christian J. Walder

This chapter discusses the use of support vector machines (SVM) for business applications. It provides a brief historical background on inductive learning and pattern recognition, and then an intuitive motivation for SVM methods. The...

Chapter 6

Kernel Width Selection for SVM Classification: A Meta-Learning Approach (pages 101-115)

Ali Smith, Kate A. Smith

The most critical component of kernel based learning algorithms is the choice of an appropriate kernel and its optimal parameters. In this paper we propose a rule based meta-learning approach for automatic radial basis function (rbf)...

Chapter 7

Protein Folding Classification Through Multicategory Discrete SVM (pages 116-130)

Carlotta Orsenigo, Carlo Vercellis

In the context of biolife science, predicting the folding structure of a protein plays an important role for investigating its function and discovering new drugs. Protein folding recognition can be naturally cast in the form of a...

Chapter 8

Hierarchical Profiling, Scoring, and Applications in Bioinformatics (pages 131-145)

Li Liao

Recently, clustering and classification methods have seen many applications in bioinformatics. Some are simply straightforward applications of existing techniques, but most have been adapted to cope with peculiar features of the...

Chapter 9

Hierarchical Clustering Using Evolutionary Algorithms (pages 146-156)

Monica Chis

Clustering is an important technique used in discovering some inherent structure present in data. The purpose of cluster analysis is to partition a given data set into a number of groups such that data in a particular cluster are...

Chapter 10

Exploratory Time Series Data Mining by Genetic Clustering (pages 157-178)

T. Warren Liao

In this chapter, we present genetic algorithm (GA) based methods developed for clustering univariate time series with equal or unequal length as an exploratory step of data mining. These methods basically implement the k-medoids...

Chapter 11

Development of Control Signatures with a Hybrid Data Mining and Genetic Algorithm (pages 179-203)

Alex Burns, Shital Shah, Andrew Kusiak

This paper presents a hybrid approach that integrates a genetic algorithm (GA) and data mining to produce control signatures. The control signatures define the best parameter intervals leading to a desired outcome. This hybrid method...

Chapter 12

Bayesian Belief Networks for Data Cleaning (pages 204-219)

Enrico Fagiuoli, Sara Omerino, Fabio Stella

The importance of data cleaning and data quality is becoming increasingly clear as evidenced by the surge in software, tools, consulting companies and seminars addressing data quality issues. In this contribution the authors present...

Chapter 13

A Comparison of Revision Schemes for Cleaning Labeling Noise (pages 220-232)

Chuck P. Lam, David G. Stork

Data quality is an important factor in building effective classifiers. One way to improve data quality is by cleaning labeling noise. Label cleaning can be divided into two stages. The first stage identifies samples with suspicious...

Chapter 14

Improving Web Clickstream Analysis: Markov Chains Models and Genmax Algorithms (pages 233-243)

Paolo Baldini, Paolo Giudici

Every time a user links up to a web site, the server keeps track of all the transactions accomplished in a log file. What is captured is the "click flow" (clickstream) of the mouse and the keys used by the user during the navigation...

Chapter 15

Advanced Data Mining and Visualization Techniques with Probabilistic Principal Surfaces: Applications to Astronomy and Genetics (pages 244-264)

Antonino Staiano, Lara De Vinco, Giuseppe Longo, Roberto Tagliaferri

Probabilistic Principal Surfaces (PPS) is a non linear latent variable model with very powerful visualization and classification capabilities which seem to be able to overcome most of the shortcomings of other neural tools. PPS...

Chapter 16

Spatial Navigation Assistance System for Large Virtual Environments: The Data Mining Approach (pages 265-283)

Mehmed Kantardzic, Pedram Sadeghian, Walaa M. Sheta

Advances in computing techniques, as well as the reduction in the cost of technology have made possible the viability and spread of large virtual environments. However, efficient navigation within these environments remains...

Chapter 17

Using Grids for Distributed Knowledge Discovery (pages 284-298)

Antonio Congiusta, Domenico Talia, Paolo Trunfio

Knowledge discovery is a compute and data intensive process that allows for finding patterns, trends, and models in large datasets. The Grid can be effectively exploited for deploying knowledge discovery applications because of the...

Chapter 18

Fuzzy Miner: Extracting Fuzzy Rules from Numerical Patterns (pages 299-321)

Nikos Pelekis, Babis Theodoulidis, Ioannis Kopanakis, Yannis Theodoridis

QOSP Quality of Service Open Shortest Path First based on QoS routing has been recognized as a missing piece in the evolution of QoS-based services in the Internet. Data mining has emerged as a tool for data analysis, discovery of...

Chapter 19

Routing Attribute Data Mining Based on Rough Set Theory (pages 322-337)

Yanbing Liu, Menghao Wang, Jong Tang

QOSPF (Quality of Service Open Shortest Path First) based on QoS routing has been recognized as a missing piece in the evolution of QoS-based services on the Internet. Data mining has emerged as a tool for data analysis, discovery of...

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