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Reseach Article

EFB Grid based Structure for Discovering Quality Clusters in Density based Clustering

by V. Kumutha, S. Palaniammal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 39 - Number 15
Year of Publication: 2012
Authors: V. Kumutha, S. Palaniammal
10.5120/4900-7443

V. Kumutha, S. Palaniammal . EFB Grid based Structure for Discovering Quality Clusters in Density based Clustering. International Journal of Computer Applications. 39, 15 ( February 2012), 43-45. DOI=10.5120/4900-7443

@article{ 10.5120/4900-7443,
author = { V. Kumutha, S. Palaniammal },
title = { EFB Grid based Structure for Discovering Quality Clusters in Density based Clustering },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 39 },
number = { 15 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 43-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume39/number15/4900-7443/ },
doi = { 10.5120/4900-7443 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:26:34.071035+05:30
%A V. Kumutha
%A S. Palaniammal
%T EFB Grid based Structure for Discovering Quality Clusters in Density based Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 39
%N 15
%P 43-45
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Clustering is one of the important data mining techniques which discover clusters in many real-world data sets. Recent algorithms attempt to find clusters in subspaces of high dimensional data. Density based clustering algorithms uses grid structure for partitioning each dimensions into intervals (bins) which yields good computation and quality results on large databases. In this paper, we propose equal-frequency based (EFB) grid structure for efficient computation of clusters for high dimensional data sets. The computation is reduced by partitioning the bins with equal frequency bin method. The performance evaluation is done with data sets taken from UCI ML Repository. The result gives better quality clusters compared with other grid structures.

References
  1. M.Ester, H.P. Kriegel, J.Sander, and X.Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. Data Mining and Knowledge Discovery, pages 226-231, 1996.
  2. M. Ankerst, M.M. Breunig, H.P. Kriegel, J. Sander,OPTICS: Ordering Points to Identify the Clustering Structure, ACM SIGMOD International Conference on Management of Data, ACM Press, pp. 49-60, 1999.
  3. Wang J. Yang, R. Muntz, STING : A Statistical Information Grid Approach to Spatial Data Mining, International Conference on Very large database, 1997.
  4. UCI repository of machine learning databases [http:/mlaern.ics.uci.edu/MLRepository.html]. 1998.
  5. C.C. Agarwal, A.Hinnerburg, and D.Keim, On the surprising behavior of Distance Metrics in High Dimensional space. ICDT, 2001.
  6. M.S.Chen, J.Han, P.S. Yu. Data mining: An Overview from Database Perspective, TKDE, 1996
  7. U. M. Fayyad, G. Piatetsky-Shapiro, P.Smyth, and R. Uthurasarmy, Advances in Knowledge Discovery and Data Mining, MIT Press, Cambridge,, MA, 1996.
  8. J.Han and M.Kamber, Data Mining: Concepts and Techniques. Morgan Kaufmann, 2000.
  9. A.K. Jain, M.N. Mutry, and P.J. Flynn, Data Clustering: A Review, ACM Computing Surveys, vol. 31, no. 3, pp. 264-323, 1999.
  10. S. Goil, H. Nagesh, and A. Choudhary, MAFIA: Efficient and Scalable Subspace Clustering for Very Large Data Sets, Technical Report CPDC-TR-9906-010, Northwestern Univ., 1999.
  11. G.Sheikholeslami, S.Chatterjee, A.Zhang, WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases, Proceedings of the International Conference on Very Large Data Bases (1998) Volume: M, Issue: 24, Publisher: Citeseer, Pages: 428-439.
  12. R.Agrawal, J. Gehrke, D.Gunopulos, P.Raghavan, Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications, Data Mining and Knowledge Discovery, vol. 11, no. 1, pp. 5-33, 2005.
  13. Ira Assent, Ralph Krieger, Emmanuel Muller, Thomas Seidl, DUSC: Dimensionality Unbiased Subspace Clustering, Proceedings IEEE Conference on Data Mining (ICDM 2007)
Index Terms

Computer Science
Information Sciences

Keywords

Cluster Dimensionality dense unit equal-frequency high dimensional