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


Many large organizations operate from multiple branches. Some of these branches collect data continuously. Thus, there are multi-branch organizations that possess multiple databases. Global decisions made by such an organization might be more appropriate if they are based on the data distributed over the branches. Moreover, the number of such applications is increasing over time. In this chapter, we discuss some of the major challenges encountered in multi-database mining that need to be dealt with. We discuss different issues of distributed data mining arising in this setting. In addition, we present three fundamental approaches to mining multiple large databases. We also elaborate on the recent developments that are taken place in this area. We provide a roadmap on how to develop an effective multi-database mining application and conclude the chapter by identifying some future research directions.


Data Mining Association Rule Local Pattern Frequent Itemsets Multiple Database 
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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