Pattern discovery using sequence data mining : applications and studies

Sequential data from Web server logs, online transaction logs, and performance measurements is collected each day. This sequential data is a valuable source of information, as it allows individuals to search for a particular value or event and also facilitates analysis of the frequency of certain events or sets of related events. Finding patterns in sequences is of utmost importance in many areas of science, engineering, and business scenarios. Pattern Discovery Using Sequence Data Mining: Applications and Studies provides a comprehensive view of sequence mining techniques and presents current research and case studies in pattern discovery in sequential data by researchers and practitioners. This research identifies industry applications introduced by various sequence mining approaches. Title......Page 2 Copyright Page......Page 3 Editorial Advisory Board......Page 4 Table of Contents......Page 5 Preface......Page 8 Section 1......Page 12 Applications of Pattern Discovery Using Sequential Data Mining......Page 14 A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns, Part I......Page 37 A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns, Part II......Page 64 Section 2......Page 85 Mining Statistically Significant Substrings Based on the Chi-Square Measure......Page 86 Unbalanced Sequential Data Classification using Extreme Outlier Elimination and Sampling Techniques......Page 96 Quantization based Sequence Generation and Subsequence Pruning for Data Mining Applications......Page 107 Classification of Biological Sequences......Page 124 Section 3......Page 149 Approaches for Pattern Discovery Using Sequential Data Mining......Page 150 Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques......Page 168 Identification of Genomic Islands by Pattern Discovery......Page 179 Video Stream Mining for On-Road Traffic Density Analytics......Page 195 Discovering Patterns in Order to Detect Weak Signals and Define New Strategies......Page 208 Discovering Patterns for Architecture Simulation by Using Sequence Mining......Page 225 Sequence Pattern Mining for Web Logs......Page 250 Compilation of References......Page 257 About the Contributors......Page 277 Index......Page 283 "This book provides a comprehensive view of sequence mining techniques, and present current research and case studies in Pattern Discovery in Sequential data authored by researchers and practitioners"-- Provided by publisher