Data Mining Patterns: New Methods and Applications, 1st Edition

  • Published By:
  • ISBN-10: 1599041642
  • ISBN-13: 9781599041643
  • DDC: 006.312
  • Grade Level Range: College Freshman - College Senior
  • 307 Pages | eBook
  • Original Copyright 2007 | Published/Released December 2007
  • This publication's content originally published in print form: 2007

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Since the introduction of the Apriori algorithm a decade ago, the problem of mining patterns is becoming a very active research area, and efficient techniques have been widely applied to the problems either in industry or science. Currently, the data mining community is focusing on new problems: mining new kinds of patterns, mining patterns under constraints, considering new kinds of complex data, and real-world applications of these concepts.

Data Mining Patterns: New Methods and Applications provides an overall view of the recent solutions for mining, and explores new kinds of patterns. This book offers theoretical frameworks and presents challenges and their possible solutions concerning pattern extractions, emphasizing both research techniques and real-world applications. Data Mining Patterns portrays research applications in data models, techniques and methodologies for mining patterns, multi-relational and multidimensional pattern mining, fuzzy data mining, data streaming, incremental mining and many other topics.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Table of Contents.
Detailed Table of Contents.
About the Editors.
1: Metric Methods in Data Mining*.
2: Bi-Directional Constraint Pushing in Frequent Pattern Mining.
3: Mining Hyperclique Patterns: A Summary of Results.
4: Pattern Discovery in Biosequences: From Simple to Complex.
5: Finding Patterns in Class-Labeled Data Using Data Visualization.
6: Summarizing Data Cubes Using Blocks.
7: Social Network Mining from the Web.
8: Discovering Spatio-Textual Association Rules in Document Images.
9: Mining XML Documents.
10: Topic and Cluster Evolution Over Noisy Document Streams.
11: Discovery of Latent Patterns with Hierarchical Bayesian Mixed-Membership Models and the Issue of Model Choice.
Compilation of References.
About the Contributors.