Research Article | Open Access | Download PDF
Volume 1 | Issue 1 | Year 2011 | Article Id. IJCTT-V1I1P10 | DOI : https://doi.org/10.14445/22312803/IJCTT-V1I1P10
A Conceptual Framework For Extending Distance Measure Algorithm For Data Clustering
A.M. Bagiwa, S.I. Dishing
Citation :
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), vol. 1, no. 1, pp. 52-54, 2011. Crossref, https://doi.org/10.14445/22312803/IJCTT-V1I1P10
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.
Keywords
Data, Cluster, Distance.
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.