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Mining Multiple Large Databases

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

Abstract

Effective data analysis using multiple databases requires highly accurate patterns. As the local pattern analysis might extract patterns of low quality from multiple databases, it becomes necessary to improve mining multiple databases. In this chapter, we present an idea of multi-database mining by making use of local pattern analysis. We elaborate on the existing specialized and generalized techniques which are used for mining multiple large databases. In the sequel, we discuss a certain generalized technique, referred to as a pipelined feedback model, which is of particular relevance for mining multiple large databases. It significantly improves the quality of the synthesized global patterns. We define two types of error occurring in multi-database mining techniques. Experimental results are provided and they are reported for both real-world and synthetic databases. They help us assess the effectiveness of the pipelined feedback model.

Keywords

Association Rule Local Pattern Mining Technique Data Warehouse Frequent Itemset 
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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