A Conceptual Framework For Extending Distance Measure Algorithm For Data Clustering

  IJCOT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© March to April Issue 2011 by IJCTT Journal
Volume-1 Issue-1                          
Year of Publication : 2011
Authors :A.M. Bagiwa, S.I. Dishing.

MLA

A.M. Bagiwa, S.I. Dishing. "A Conceptual Framework For Extending Distance Measure Algorithm For Data Clustering "International Journal of Computer Trends and Technology (IJCTT),V1(1):52-54 March to April Issue 2011 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract—In this paper we look at data clustering as a problem that involves finding the relationship between data sets. The framework for this paper introduces an enhancement of the distance measure data clustering algorithm by adding some prior knowledge describing the domain of clusters. In this work we first take an overview of different data clustering algorithms. We then propose our approach for data clustering as an enhancement to the distance measure algorithm.

References-

[1]. R. Ng and J. Han, “CLARANS: A Method for Clustering Objects for Spatial Data Mining,” IEEE Trans. Knowledge and Data Eng., vol. 14, no. 5, pp. 1003-1016, Sept./Oct. 2002.
[2] J. C. Dunn (1973): "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters", Journal of Cybernetics 3: 32- 57
[3]. J. C. Bezdek (1981): "Pattern Recognition with Fuzzy Objective Function Algoritms", Plenum Press, New York
[4]. J. B. MacQueen (1967): "Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability", Berkeley, University of California Press, 1:281-297
[5]. C. Gozzi, F. Giannotti, and G. Manco, “Clustering Transactional Data,” Proc. Sixth European Conf. Principles and Practice of Knowledge Discovery in Databases (PKDD ’02), pp. 175-187, 2002.

Keywords— Data, Cluster, Distance.