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| Finding Relationships between the Our-NIR Cluster Results
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Full
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Source |
International Journal of Computer Science and Security (IJCSS) |
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Table of Contents |
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Complete Issue PDF(2.93MB) |
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Volume: 5 Issue: 3 |
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Pages: 298-393 |
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Publication
Date: July / August 2011 |
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ISSN
(Online): 1985-1553 |
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Pages |
387 - 393 |
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Author(s) |
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Published
Date |
05-08-2011 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
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| The problem of evaluating node importance in clustering has been active research in present days and many methods have been developed. Most of the clustering algorithms deal with general similarity measures. However In real situation most of the cases data changes over time. But clustering this type of data not only decreases the quality of clusters but also disregards the expectation of users, when usually require recent clustering results. In this regard we proposed Our-NIR method that is better than Ming-Syan Chen proposed a method and it has proven with the help of results of node importance, which is related to calculate the node importance that is very useful in clustering of categorical data, still it has deficiency that is importance of data labeling and outlier detection. In this paper we modified Our-NIR method for evaluating of node importance by introducing the probability distribution which will be better than by comparing the results. |
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| 1 |
C. Aggarwal, J. Han, J. Wang, and P. Yu, “A Framework for Clustering Evolving Data Streams,” Proc. 29th Int'l Conf.Very Large Data Bases (VLDB) ,2003.
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| 2 |
C.C. Aggarwal, J.L. Wolf, P.S. Yu, C. Procopiuc, and J.S. Park, “Fast Algorithms for Projected Clustering,” Proc. ACM SIGMOD” 1999, pp. 61-72.
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| 3 |
P. Andritsos, P. Tsaparas, R.J. Miller, and K.C. Sevcik, “Limbo: Scalable Clustering of Categorical Data,” Proc. Ninth Int'l Conf. Extending Database Technology (EDBT), 2004.
|
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|
| 4 |
D. Barbará, Y. Li, and J. Couto, “Coolcat: An Entropy-Based Algorithm for Categorical Clustering,” Proc. ACM Int'l Conf. Information and Knowledge Management (CIKM), 2002.
|
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| 5 |
F. Cao, M. Ester, W. Qian, and A. Zhou, “Density-Based Clustering over an Evolving Data Stream with Noise,” Proc. Sixth SIAM Int'l Conf. Data Mining (SDM), 2006.
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| 6 |
D. Chakrabarti, R. Kumar, and A. Tomkins, “Evolutionary Clustering,”Proc. ACM SIGKDD” 2006, pp. 554-560..
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| 7 |
H.-L. Chen, K.-T. Chuang and M.-S. Chen, “Labeling Unclustered Categorical Data into Clusters Based on the Important Attribute Values,” Proc. Fifth IEEE Int'l Conf. Data Mining (ICDM), 2005.
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| 8 |
H.-L. Chen, M.-S. Chen, and S-U Chen Lin “Frame work for clustering Concept –Drifting categorical data,” IEEE Transaction Knowledge and Data Engineering v21 no 5 , 2009.
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| 9 |
D.H. Fisher, “Knowledge Acquisition via Incremental Conceptual Clustering,” Machine Learning, 1987.
|
|
|
| 10 |
Fan, W. Systematic data selection to mine concept-drifting data streams. in Tenth ACM SIGKDD international conference on Knowledge Discovery and Data Mining. 2004. Seattle, WA, USA: ACM Press: p. 128-137.
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| 11 |
MM Gaber and PS Yu “Detection and Classification of Changes in Evolving Data Streams,” International .Journal .Information Technology and Decision Making, v5 no 4, 2006.
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| 12 |
M.A. Gluck and J.E. Corter, “Information Uncertainty and the Utility of Categories,” Proc. Seventh Ann. Conf. Cognitive Science Soc., pp. 283-287, 1985.
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| 13 |
G Hulton and Spencer, “Mining Time-Changing Data Streams” Proc. ACM SIGKDD, 2001.
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| 14 |
AK Jain MN Murthy and P J Flyn “Data Clustering: A Review,” ACM Computing Survey, 1999.
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| 15 |
Klinkenberg, R., Learning Drifting Concepts: Example Selection vs. Exam- ple Weighting Intelligent Data Analysis, Special Issue on Incremental Learn- ing Systems Capable of Dealing with Concept Drift, 2004. 8(3): p. 281-200.
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| 16 |
O.Narsoui and C.Rojas,“Robust Clustering for Tracking Noisy Evolving Data Streams” SIAM Int. Conference Data Mining , 2006.
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| 17 |
C.E. Shannon, “A Mathematical Theory of Communication,” Bell System Technical J., 1948.
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| 18 |
Viswanadha Raju, H.Venkateswara Reddy andN.Sudhakar Reddy,” A Threshold for clustering Concept – Drifting Categorical Data”, IEEE Computer Society, ICMLC 2011.
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S.Viswanadha Raju,H.Venkateswara Reddy and N.Sudhakar Reddy ” Our-NIR:Node Importance Representation of Clustering Categorical Data ”, IJCST June 2011.
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S.Viswanadha Raju, N.Sudhakar Reddy and H.Venkateswara Reddy,” Clustering of Concept Drift Categorical Data using Our-NIR Method, IJEE 2011
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| N.Sudhakar Reddy : Colleagues
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