Mining Multiple Clustering Data for Knowledge Discovery
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.
KeywordsCluster Method Data Item Mahalanobis Distance Document Cluster Combine Vector
Unable to display preview. Download preview PDF.
- 1.Berkhin, P.: Survey of Clustering Data Mining Techniques. Technical Report. Accrue Soft-ware, Inc (2002)Google Scholar
- 3.Van Rijsbergen, C.: Information Retrieval. Utterworths, London (1979)Google Scholar
- 7.Carkacioglu, A., Vural, F.Y.: Learning Similarity Space. In: International Conference on Image Processing, pp. 405–408 (2002)Google Scholar
- 8.Weinberg, S.: Applied linear regression. John Wiley and Sons, Chichester (1985)Google Scholar
- 9.Everitt, B.: Cluster Analysis, 3rd edn. Edward Arnold, London (1993)Google Scholar
- 12.Zamir, O., Etzioni, O.: Web Document Clustering: a Feasibility Demonstration. In: Proceeding of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 46–54 (1998)Google Scholar
- 14.Grossberg, S.: The Adaptive Self-Organization of Serial Order in Behavior: Speech, Language and Motor Control. In: Pattern Recognition By Humans and Machines, vol. I, Speech Perception. Academic Press Inc., London (1986)Google Scholar