Data Analysis and Pattern Recognition in Multiple Databases, 1st Edition

  • Published By:
  • ISBN-10: 3319034103
  • ISBN-13: 9783319034102
  • DDC: 006.312
  • Grade Level Range: College Freshman - College Senior
  • 238 Pages | eBook
  • Original Copyright 2014 | Published/Released June 2014
  • This publication's content originally published in print form: 2014

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Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.

Table of Contents

Front Cover.
Editorial Board.
Other Frontmatter.
Title Page.
Copyright Page.
1: Introduction.
2: Synthesizing Different Extreme Association Rules from Multiple Databases.
3: Clustering Items in Time-Stamped Databases Induced by Stability.
4: Synthesizing Global Patterns in Multiple Large Data Sources.
5: Clustering Local Frequency Items in Multiple Data Sources.
6: Mining Patterns of Select Items in Different Data Sources.
7: Synthesizing Global Exceptional Patterns in Different Data Sources.
8: Mining Icebergs in Different Time-Stamped Data Sources.
9: Mining Calendar-Based Periodic Patterns in Time-Stamped Data.
10: Measuring Influence of an Item in Time-Stamped Databases.
11: Summary and Conclusions.