We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

A Novel Approach for Clustering based on Pattern Analysis

by Prachi M. Joshi, Dr. Parag A. Kulkarni
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 25 - Number 4
Year of Publication: 2011
Authors: Prachi M. Joshi, Dr. Parag A. Kulkarni
10.5120/3023-4089

Prachi M. Joshi, Dr. Parag A. Kulkarni . A Novel Approach for Clustering based on Pattern Analysis. International Journal of Computer Applications. 25, 4 ( July 2011), 1-4. DOI=10.5120/3023-4089

@article{ 10.5120/3023-4089,
author = { Prachi M. Joshi, Dr. Parag A. Kulkarni },
title = { A Novel Approach for Clustering based on Pattern Analysis },
journal = { International Journal of Computer Applications },
issue_date = { July 2011 },
volume = { 25 },
number = { 4 },
month = { July },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume25/number4/3023-4089/ },
doi = { 10.5120/3023-4089 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:10:51.558559+05:30
%A Prachi M. Joshi
%A Dr. Parag A. Kulkarni
%T A Novel Approach for Clustering based on Pattern Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 25
%N 4
%P 1-4
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Clustering aims at grouping of data into clusters based on the similarity between them. It is the pattern of the data that governs grouping. In this paper, we propose method for clustering that is based on finding closeness between the data series. A novel method referred as Clustering with Closeness factor (CCF) is proposed that works in two phases and is not pre-bound with clusters numbers. The method identifies the pattern of data and performs clustering. With proper selection of threshold value, the approach can prove to be a big step for decision making.

References
  1. Camastra, F. and Verri, A. 2002. A novel kernel method for clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, p 22-32.
  2. Kanung, T. Netanyahu, N. and Wu, A. 2002. An efficient k-means clustering algorithm, analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, p 881-892.
  3. Kulkarni, P., Kulkarni, M. 2002. Advance forecasting methods for resource management and medical decision-making. In Proceedings of National Conference on Logistics Management: Future Trends.
  4. Becker, H., Namman M., and Gravano, L. 2010. Learning similarity metrics for event identification in social media. In Proceedings of the Third ACM International Conference on Web Search and Data Mining, USA, p 291-300.
  5. Bharti, K., Jain, S., and Shukla, S. 2010. Fuzzy k-means clustering viaJ48 for intrusion detection system. International Journal of Computer Science and Information Technologies, Vol. 1, p 315-318.
  6. Lui, Y., Cai, J., Yin, J., and Fu, A. 2008. Clustering text data streams. Journal of Computer Science and Technology,( Jan 2008) , p 112-128.
  7. Deelers, S., Auwantanamongkol, S. 2007. Enhancing k-means algorithm with initial cluster centers derived from data partitioning along the data axis with highest variance. International Journal of Computer Science.
  8. Fahim, A., Saake, G., Salem, A., Torky, F., and Ramadam, M. 2008. K-means for spherical clusters with large variance in sizes. Journal of World Academy of Science, Engineering and Technology.
  9. Jain, A., Murthy, M., and Flynn, P. 1999. Data clustering: A review. ACM Computing Surveys, Vol. 31, No. 3.
  10. Lai, J., Huang, T., and Liaw, Y. 2009. A fast k-means clustering algorithm using cluster center displacement. Journal of Pattern Recognition, Vol. 41, No. 1, (Nov 2009), p 2551-2556.
  11. Chandra, E., Anuradha, V. 2011. A survey of clustering algorithms for data in spatial database management systems. International Journal of Computer Applications, Vol. 24, No. 9.
  12. Dave, R., and Krishnapuram, R. 1997. Robust clustering methods: A unified view. IEEE transactions on fuzzy systems, Vol. 5, No. 2.
  13. Geva, A. 1999. Hierarchical unsupervised fuzzy clustering. IEEE Transactions on Fuzzy Systems, Vol. 7, No. 6.
  14. Yank, M., and Wu, K. 2004. A similarity based robust clustering method. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 4.
  15. Cai, D., He, X., and Han, J. 2011. Locally consistent concept factorization for document clustering. IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 6, 902 – 913.
  16. He, Q., Chang, K., Lim E., and Banerjee, A. 2009. Keep it simple with time: A Re-examination of probabilistic topic detection models. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 10, No. 10.
  17. Zhang, K., Tsang, I., and Kwok, J. 2009. Maximum margin clustering made practical. IEEE Transactions on Neural Networks, Vol. 20, No. 4, (April 2009).
  18. Frank, A., Asuncion, A. 2010. UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
Index Terms

Computer Science
Information Sciences

Keywords

Clustering closeness threshold k-means