Mining Multiple Clustering Data for Knowledge Discovery

  • Thanh Tho Quan
  • Siu Cheung Hui
  • Alvis Fong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2843)


Clustering has been widely used for knowledge discovery. In this paper, we propose an effective approach known as Multi-Clustering to mine the data generated from different clustering methods for discovering relationships between clusters of data. In the proposed Multi-Clustering technique, it first generates combined vectors from the multiple clustering data. Then, the distances between the combined vectors are calculated using the Mahalanobis distance. The Agglomerative Hierarchical Clustering method is used to cluster the combined vectors. And finally, relationship vectors that can be used to identify the cluster relationships are generated. To illustrate the technique, we also discuss an application example that uses the proposed Multi-Clustering technique to mine the author clusters and document clusters for identifying the relationships on authors working on research areas. The performance of the proposed technique is also evaluated.


Cluster Method Data Item Mahalanobis Distance Document Cluster Combine Vector 
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 Berlin Heidelberg 2003

Authors and Affiliations

  • Thanh Tho Quan
    • 1
  • Siu Cheung Hui
    • 1
  • Alvis Fong
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore

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