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Enhancing Quality of Knowledge Synthesized from Multi-database Mining

  • Animesh AdhikariEmail author
  • Pralhad Ramachandrarao
  • Witold Pedrycz
Chapter
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

Multi-database mining using local pattern analysis could be considered as an approximate method of mining multiple large databases. Assuming this point of view, it might be required to enhance the quality of knowledge synthesized from multiple databases. Also, many decision-making applications are directly based on the available local patterns present in different databases. The quality of synthesized knowledge/decision based on local patterns present in different databases could be enhanced by incorporating more local patterns in the knowledge synthesizing/processing activities. Thus, the available local patterns play a crucial role in building efficient multi-database mining applications. We represent patterns in a condensed form by employing a so-called ACP (antecedent-consequent pair) coding. It allows one to consider more local patterns by lowering further the user-defined characteristics of discovered patterns, like minimum support and minimum confidence. The ACP coding enables more local patterns participate in the knowledge synthesizing/processing activities and thus the quality of synthesized knowledge based on local patterns becomes enhanced significantly with regard to the synthesizing algorithm and required computing resources. To secure a convenient access to association rule, we introduce an index structure. We demonstrate that ACP coding represents rulebases by making use of the least amount of storage space in comparison to any other rulebase representation technique. Furthermore we present a technique for storing rulebases in the secondary storage.

Keywords

Association Rule Main Memory Storage Space Local Pattern Minimum Support 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2010

Authors and Affiliations

  • Animesh Adhikari
    • 1
    Email author
  • Pralhad Ramachandrarao
    • 2
  • Witold Pedrycz
    • 3
  1. 1.Department of Computer ScienceSmt. Parvatibal Chowgule CollegeMargoaIndia
  2. 2.Department of Computer Science & TechnologyGoa UniversityGoaIndia
  3. 3.Department of Electrical & Computer EngineeringUniversity of AlbertaEdmontonCanada

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