A Probabilistic Estimation of Cluster Region Prone to Inter Cluster Data Movement

Authors

  • AM Rajee Department of CSE, Pondicherry Engineering College, India
  • F Sagayaraj Francis Department of CSE, Pondicherry Engineering College, India

Keywords:

Data Clustering, Inter Cluster Data Movement, Probabilistic Model, Un-Clustered Information

Abstract

Data clustering is an unsupervised learning methodology. We consider the problem of dealing with unclustered information to the already existing clustering setup. The new entrée may cause movement of data points between clusters, thereby altering the dynamics of the clustering system. With this scenario, this paper attempts to predict the region in the cluster which will facilitate such inter cluster data movement. A probabilistic model was built which will estimate the region, which has higher chance for enabling the data objects to move in and out of the cluster. Experimental studies were made with multiple instances of synthetic two dimensional data sets. The observed values were compared with the predicted values and the results displayed improved accuracy of the probabilistic model.

References

J. Han and M.Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann Publishers, 2001.

S.Lloyd, “Least squares quantization in PCM”, IEEE Transactions on Information Theory, 1982, pp.129-136.

A. Campan and G. Serban, “Adaptive Clustering algorithms”, Advances in Artificial Intelligence, Springer, 2006.

G.Serban and A.Campan, “Adaptive Clustering using a Core-based Approach”, Informatica, Volume L, Number 2, 2005.

Charu C. Aggarwal, Philip S. Yu, “A Framework for Clustering Massive Text and Categorical Data Streams”, ACM SIAM Data Mining Conference, 2006

Angie King, “Online k-Means Clustering of Non-stationary Data”, Prediction Project Report, 2012

Seokkyung Chung and Dennis McLeod, “Dynamic Pattern Mining: An Incremental Data Clustering Approach”, Journal on Data Semantics, Volume 2, 2005

A.M.Rajee and F.Sagayaraj Francis, “Inter Cluster Movement Estimation model based on cluster parameters”, in Proc. IEEE International Conference on Computational Intelligence and Computing Research”, 2013, pp.369-372.

Jain A. K, “Data Clustering: 50 Years Beyond K-means”, Pattern Recognition Letters 31(8), 2010, pp.651–666.

Jain A. K, Murty M. N and Flynn, P. J, “Data Clustering: A Review. ACM Computing Surveys”, 31(3), 1999, pp. 264–323.

Downloads

Published

2014-12-06

How to Cite

[1]
A. Rajee and F. Sagayaraj Francis, “A Probabilistic Estimation of Cluster Region Prone to Inter Cluster Data Movement”, Int. J. Comp. Sci. Eng., vol. 2, no. 11, pp. 138–141, Dec. 2014.

Issue

Vol. 2 No. 11 (2014): IJCSE November Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.