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

A. M. Rajee1 , F. Sagayaraj Francis2

Section:Research Paper, Product Type: Journal Paper
Volume-2 , Issue-11 , Page no. 138-141, Nov-2014

Online published on Nov 30, 2014

Copyright © A. M. Rajee , F. Sagayaraj Francis . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: A. M. Rajee , F. Sagayaraj Francis, “A Probabilistic Estimation of Cluster Region Prone to Inter Cluster Data Movement,” International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.138-141, 2014.

MLA Style Citation: A. M. Rajee , F. Sagayaraj Francis "A Probabilistic Estimation of Cluster Region Prone to Inter Cluster Data Movement." International Journal of Computer Sciences and Engineering 2.11 (2014): 138-141.

APA Style Citation: A. M. Rajee , F. Sagayaraj Francis, (2014). A Probabilistic Estimation of Cluster Region Prone to Inter Cluster Data Movement. International Journal of Computer Sciences and Engineering, 2(11), 138-141.

BibTex Style Citation:
@article{Rajee_2014,
author = {A. M. Rajee , F. Sagayaraj Francis},
title = {A Probabilistic Estimation of Cluster Region Prone to Inter Cluster Data Movement},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {11 2014},
volume = {2},
Issue = {11},
month = {11},
year = {2014},
issn = {2347-2693},
pages = {138-141},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=318},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=318
TI - A Probabilistic Estimation of Cluster Region Prone to Inter Cluster Data Movement
T2 - International Journal of Computer Sciences and Engineering
AU - A. M. Rajee , F. Sagayaraj Francis
PY - 2014
DA - 2014/11/30
PB - IJCSE, Indore, INDIA
SP - 138-141
IS - 11
VL - 2
SN - 2347-2693
ER -

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Abstract

[1] J. Han and M.Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann Publishers, 2001. [2] S.Lloyd, “Least squares quantization in PCM”, IEEE Transactions on Information Theory, 1982, pp.129-136. [3] A. Campan and G. Serban, “Adaptive Clustering algorithms”, Advances in Artificial Intelligence, Springer, 2006. [4] G.Serban and A.Campan, “Adaptive Clustering using a Core-based Approach”, Informatica, Volume L, Number 2, 2005. [5] Charu C. Aggarwal, Philip S. Yu, “A Framework for Clustering Massive Text and Categorical Data Streams”, ACM SIAM Data Mining Conference, 2006 [6] Angie King, “Online k-Means Clustering of Non-stationary Data”, Prediction Project Report, 2012 [7] Seokkyung Chung and Dennis McLeod, “Dynamic Pattern Mining: An Incremental Data Clustering Approach”, Journal on Data Semantics, Volume 2, 2005 [8] 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. [9] Jain A. K, “Data Clustering: 50 Years Beyond K-means”, Pattern Recognition Letters 31(8), 2010, pp.651–666. [10] Jain A. K, Murty M. N and Flynn, P. J, “Data Clustering: A Review. ACM Computing Surveys”, 31(3), 1999, pp. 264–323.

Key-Words / Index Term

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

References

[1] J. Han and M.Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann Publishers, 2001.
[2] S.Lloyd, “Least squares quantization in PCM”, IEEE Transactions on Information Theory, 1982, pp.129-136.
[3] A. Campan and G. Serban, “Adaptive Clustering algorithms”, Advances in Artificial Intelligence, Springer, 2006.
[4] G.Serban and A.Campan, “Adaptive Clustering using a Core-based Approach”, Informatica, Volume L, Number 2, 2005.
[5] Charu C. Aggarwal, Philip S. Yu, “A Framework for Clustering Massive Text and Categorical Data Streams”, ACM SIAM Data Mining Conference, 2006
[6] Angie King, “Online k-Means Clustering of Non-stationary Data”, Prediction Project Report, 2012
[7] Seokkyung Chung and Dennis McLeod, “Dynamic Pattern Mining: An Incremental Data Clustering Approach”, Journal on Data Semantics, Volume 2, 2005
[8] 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.
[9] Jain A. K, “Data Clustering: 50 Years Beyond K-means”, Pattern Recognition Letters 31(8), 2010, pp.651–666.
[10] Jain A. K, Murty M. N and Flynn, P. J, “Data Clustering: A Review. ACM Computing Surveys”, 31(3), 1999, pp. 264–323.