Pattern Discovery Using Sequence Data Mining: Applications and Studies Front Cover

Pattern Discovery Using Sequence Data Mining: Applications and Studies

  • Length: 286 pages
  • Edition: 1
  • Publisher:
  • Publication Date: 2011-09-30
  • ISBN-10: 1613500564
  • ISBN-13: 9781613500569
  • Sales Rank: #9468847 (See Top 100 Books)
Description

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.

Table of Contents

Section 1 Current State of Art
Chapter 1 Applications of Pattern Discovery Using Sequential Data Mining
Chapter 2 A Review of Kernel Methods Based Approaches to Classifcation and Clustering of Sequential, Part I: Sequences of Continuous Feature Vectors
Chapter 3 A Review of Kernel Methods Based Approaches to Classifcation and Clustering of Sequential, Part II: Sequences of Discrete Symbols

Section 2 Techniques
Chapter 4 Mining Statistically Signifcant Substrings Based on the Chi-Square Measure
Chapter 5 Unbalanced Sequential Data Classifcation Using Extreme Outlier Elimination and Sampling Techniques
Chapter 6 Quantization Based Sequence Generation and Subsequence Pruning for Data Mining Applications
Chapter 7 Classifcation of Biological Sequences

Section 3 Applications
Chapter 8 Approaches for Pattern Discovery Using Sequential Data Mining
Chapter 9 Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques
Chapter 10 Identifcation of Genomic Islands by Pattern Discovery
Chapter 11 Video Stream Mining for On-Road Traffc Density Analytics
Chapter 12 Discovering Patterns in Order to Detect Weak Signals and Defne New Strategies
Chapter 13 Discovering Patterns for Architecture Simulation by Using Sequence Mining
Chapter 14 Sequence Pattern Mining for Web Logs

To access the link, solve the captcha.