A Comprehensive Review on Different Mixed Data Clustering Ensemble Methods
An extensive amount of work has been done in data
clustering research under the unsupervised learning technique in Data
Mining during the past two decades. Moreover, several approaches
and methods have been emerged focusing on clustering diverse data
types, features of cluster models and similarity rates of clusters.
However, none of the single clustering algorithm exemplifies its best
nature in extracting efficient clusters. Consequently, in order to
rectify this issue, a new challenging technique called Cluster
Ensemble method was bloomed. This new approach tends to be the
alternative method for the cluster analysis problem. The main
objective of the Cluster Ensemble is to aggregate the diverse
clustering solutions in such a way to attain accuracy and also to
improve the eminence the individual clustering algorithms. Due to
the massive and rapid development of new methods in the globe of
data mining, it is highly mandatory to scrutinize a vital analysis of
existing techniques and the future novelty. This paper shows the
comparative analysis of different cluster ensemble methods along
with their methodologies and salient features. Henceforth this
unambiguous analysis will be very useful for the society of clustering
experts and also helps in deciding the most appropriate one to resolve
the problem in hand.
[1] S. Sarumathi, N. Shanthi, M. Sharmila. "A Review: Comparative
Analysis of Different Categorical Data Clustering Ensemble Methods”.
International Journal of Computer, Information Science and Engineering
Vol:7 No:12, 2013.
[2] Sandro Vega-pons & Jose reuizShulcloper. "A Survey of Clustering
Ensemble algorithms”. International Journal of Pattern Recognition and
Artificial Intelligence Vol. 25, No. 3 337_372 2011.
[3] NatthakanIam-On, TossaponBoongoen, Simon Garrett, & Chris Price.
"A Link based cluster ensemble approach for categorical data
clustering”. IEEE Transactions on knowledge and data engineering, Vol.
24, No. 3, 2012.
[4] HarunPirim, DilipGautam, Tanmay ,Bhowmik, Andy D. Perkins,
BurakEkşioglu, &AhmetAlkan,. "Performance of an ensemble clustering
algorithm on biological datasets”. Mathematical and Computational
Applications, Vol. 16, No. 1, pp. 87-96 2011.
[5] MajidMahrooghy, Nicolas H. Younan, Valentine G. Anantharaj, James
Aanstoos, and ShantiaYarahmadian, "On the Use of a Cluster Ensemble
Cloud Classification Technique in Satellite Precipitation Estimation”,
IEEE journal of selected topics in applied earth observations and remote
sensing, vol. 5, no. 5, october 2012.
[6] Y. Hong, K. L. Hsu, S. Sorooshian, and X. G. Gao, "Precipitation
estimation from remotely sensed imagery using an artificial neural
network cloud classification system,” J. Appl. Meteorol., vol. 43, pp.
1834–1852, 2004
[7] J. C. Grzegorz and W. F. Krajewski, "Comments on the window
probability matching method for rainfall measurements with radar,” J.
Appl. Meteorol., vol. 36, pp. 243–246, 1997.
[8] M. Mahrooghy, V. G. Anantharaj, N. H. Younan, W. A. Petersen, F.
J.Turk, and J. Aanstoos, "Infrared satellite precipitation estimate using
waveletbased cloud classification and radar calibration,” in Proc. 2010
IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), 2010, pp.
2345–2348.
[9] N. Iam-on, T. Boongoen, and S. Garrett, "LCE: A link-based cluster
ensemble method for improved gene expression data analysis,”
Bioinformatics, vol. 26, pp. 1513–1519, 2010
[10] R. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. New York:
Wiley-Interscience, 2000.
[11] D. A. Neumann and V. Norton, "Clustering and isolation in the
consensus problem for partitions,” J. Classification, vol. 3, pp. 281–297,
1986.
[12] Jing Gao, Feng Liang, Wei Fan, Yizhou Sun, and Jiawei Han, "A Graph-
Based Consensus Maximization Approach for Combining Multiple
Supervised and Unsupervised Models” , IEEE transactions on
knowledge and data engineering, vol. 25, no. 1, january 2013.
[13] D.P. Bertsekas, Non-Linear Programming, second ed. Athena Scientific,
1999.
[14] NatthakanIam-On, TossaponBoongoen, "Improved Link-Based Cluster
Ensembles”, IEEE World Congress on Computational Intelligence June,
10-15, 2012.
[15] N Iam-on, "LCE: A link-based cluster ensemble method for improved
gene expression data analysis,” Bioinformatics, vol. 26, no. 12, pp.
1513– 1519, 2010
[16] N. Iam-On, T. Boongoen, S. Garrett, and C. Price, "A link-based
approach to the cluster ensemble problem,” IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2396–
2409, 2011.
[17] N. Iam-On, T. Boongoen, and S. Garrett, "Refining pairwise similarity
matrix for cluster ensemble problem with cluster relations,” in
Proceedings of Eleventh International Conference on Discovery Science,
2008,pp. 222–233.
[18] T. Boongoen, Q. Shen, and C. Price, "Disclosing false identity through
hybrid link analysis,” AI and Law, vol. 18, no. 1, pp. 77–102, 2010.
[19] A. Ng, M. Jordan, and Y. Weiss, "On spectral clustering: Analysis and
an algorithm,” Advances in Neural Information Processing Systems, vol.
14, pp. 849–856, 2001.
[20] Xiaoli Z. Fern and Carla E. Brodley, "Solving cluster ensemble
problems by bipartite graph partitioning,” in Proceedings of
International Conference on Machine Learning, 2004, pp. 36–43.
[21] Tsaipei Wang, "CA-Tree: A Hierarchical Structure for Efficient and
Scalable Coassociation-Based Cluster Ensembles”, IEEE transactions on
systems, man, and cybernetics—part b: cybernetics, vol. 41, no. 3, june
2011.
[22] T.-Y. Lv, S.-B. Huang, X.-Z. Zhang, and Z.-X. Wang, "Combining
multiple clustering methods based on core group,” in Proc. 2nd Int.
Conf.Semantics, Knowl., Grid, 2006, pp. 29–34.
[23] Yun Yang and Ke Chen, "Temporal Data Clustering via Weighted
Clustering Ensemble with Different Representations”, IEEE transactions
on knowledge and data engineering, vol. 23, no. 2, february 2011.
[24] A. Jain, M. Murthy, and P. Flynn, "Data Clustering: A Review,” ACM
Computing Surveys, vol. 31, pp. 264-323, 1999.
[25] M. Halkidi, Y. Batistakis, and M. Varzirgiannis, "On Clustering
Validation Techniques,” J. Intelligent Information Systems, vol. 17, pp.
107-145, 2001
[26] A. Strehl and J. Ghosh, "Cluster Ensembles—A Knowledge Reuse
Framework for Combining Multiple Partitions,” J. Machine Learning
Research, vol. 3, pp. 583-617, 2002.
[27] A. Fred and A. Jain, "Combining Multiple Clusterings Using Evidence
Accumulation,” IEEE Trans. Pattern Analysis and Machine Intelligence,
vol. 27, no. 6 pp. 835-850, June 2005.
[28] Sarumathi S, Shanthi N, Sharmila M. " A Comparative Analysis of
Different Categorical Data Clustering Ensemble Methods in
DataMining” International Journal of Computer Applications Vol 81,
No.4 November 2013.
[1] S. Sarumathi, N. Shanthi, M. Sharmila. "A Review: Comparative
Analysis of Different Categorical Data Clustering Ensemble Methods”.
International Journal of Computer, Information Science and Engineering
Vol:7 No:12, 2013.
[2] Sandro Vega-pons & Jose reuizShulcloper. "A Survey of Clustering
Ensemble algorithms”. International Journal of Pattern Recognition and
Artificial Intelligence Vol. 25, No. 3 337_372 2011.
[3] NatthakanIam-On, TossaponBoongoen, Simon Garrett, & Chris Price.
"A Link based cluster ensemble approach for categorical data
clustering”. IEEE Transactions on knowledge and data engineering, Vol.
24, No. 3, 2012.
[4] HarunPirim, DilipGautam, Tanmay ,Bhowmik, Andy D. Perkins,
BurakEkşioglu, &AhmetAlkan,. "Performance of an ensemble clustering
algorithm on biological datasets”. Mathematical and Computational
Applications, Vol. 16, No. 1, pp. 87-96 2011.
[5] MajidMahrooghy, Nicolas H. Younan, Valentine G. Anantharaj, James
Aanstoos, and ShantiaYarahmadian, "On the Use of a Cluster Ensemble
Cloud Classification Technique in Satellite Precipitation Estimation”,
IEEE journal of selected topics in applied earth observations and remote
sensing, vol. 5, no. 5, october 2012.
[6] Y. Hong, K. L. Hsu, S. Sorooshian, and X. G. Gao, "Precipitation
estimation from remotely sensed imagery using an artificial neural
network cloud classification system,” J. Appl. Meteorol., vol. 43, pp.
1834–1852, 2004
[7] J. C. Grzegorz and W. F. Krajewski, "Comments on the window
probability matching method for rainfall measurements with radar,” J.
Appl. Meteorol., vol. 36, pp. 243–246, 1997.
[8] M. Mahrooghy, V. G. Anantharaj, N. H. Younan, W. A. Petersen, F.
J.Turk, and J. Aanstoos, "Infrared satellite precipitation estimate using
waveletbased cloud classification and radar calibration,” in Proc. 2010
IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), 2010, pp.
2345–2348.
[9] N. Iam-on, T. Boongoen, and S. Garrett, "LCE: A link-based cluster
ensemble method for improved gene expression data analysis,”
Bioinformatics, vol. 26, pp. 1513–1519, 2010
[10] R. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. New York:
Wiley-Interscience, 2000.
[11] D. A. Neumann and V. Norton, "Clustering and isolation in the
consensus problem for partitions,” J. Classification, vol. 3, pp. 281–297,
1986.
[12] Jing Gao, Feng Liang, Wei Fan, Yizhou Sun, and Jiawei Han, "A Graph-
Based Consensus Maximization Approach for Combining Multiple
Supervised and Unsupervised Models” , IEEE transactions on
knowledge and data engineering, vol. 25, no. 1, january 2013.
[13] D.P. Bertsekas, Non-Linear Programming, second ed. Athena Scientific,
1999.
[14] NatthakanIam-On, TossaponBoongoen, "Improved Link-Based Cluster
Ensembles”, IEEE World Congress on Computational Intelligence June,
10-15, 2012.
[15] N Iam-on, "LCE: A link-based cluster ensemble method for improved
gene expression data analysis,” Bioinformatics, vol. 26, no. 12, pp.
1513– 1519, 2010
[16] N. Iam-On, T. Boongoen, S. Garrett, and C. Price, "A link-based
approach to the cluster ensemble problem,” IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2396–
2409, 2011.
[17] N. Iam-On, T. Boongoen, and S. Garrett, "Refining pairwise similarity
matrix for cluster ensemble problem with cluster relations,” in
Proceedings of Eleventh International Conference on Discovery Science,
2008,pp. 222–233.
[18] T. Boongoen, Q. Shen, and C. Price, "Disclosing false identity through
hybrid link analysis,” AI and Law, vol. 18, no. 1, pp. 77–102, 2010.
[19] A. Ng, M. Jordan, and Y. Weiss, "On spectral clustering: Analysis and
an algorithm,” Advances in Neural Information Processing Systems, vol.
14, pp. 849–856, 2001.
[20] Xiaoli Z. Fern and Carla E. Brodley, "Solving cluster ensemble
problems by bipartite graph partitioning,” in Proceedings of
International Conference on Machine Learning, 2004, pp. 36–43.
[21] Tsaipei Wang, "CA-Tree: A Hierarchical Structure for Efficient and
Scalable Coassociation-Based Cluster Ensembles”, IEEE transactions on
systems, man, and cybernetics—part b: cybernetics, vol. 41, no. 3, june
2011.
[22] T.-Y. Lv, S.-B. Huang, X.-Z. Zhang, and Z.-X. Wang, "Combining
multiple clustering methods based on core group,” in Proc. 2nd Int.
Conf.Semantics, Knowl., Grid, 2006, pp. 29–34.
[23] Yun Yang and Ke Chen, "Temporal Data Clustering via Weighted
Clustering Ensemble with Different Representations”, IEEE transactions
on knowledge and data engineering, vol. 23, no. 2, february 2011.
[24] A. Jain, M. Murthy, and P. Flynn, "Data Clustering: A Review,” ACM
Computing Surveys, vol. 31, pp. 264-323, 1999.
[25] M. Halkidi, Y. Batistakis, and M. Varzirgiannis, "On Clustering
Validation Techniques,” J. Intelligent Information Systems, vol. 17, pp.
107-145, 2001
[26] A. Strehl and J. Ghosh, "Cluster Ensembles—A Knowledge Reuse
Framework for Combining Multiple Partitions,” J. Machine Learning
Research, vol. 3, pp. 583-617, 2002.
[27] A. Fred and A. Jain, "Combining Multiple Clusterings Using Evidence
Accumulation,” IEEE Trans. Pattern Analysis and Machine Intelligence,
vol. 27, no. 6 pp. 835-850, June 2005.
[28] Sarumathi S, Shanthi N, Sharmila M. " A Comparative Analysis of
Different Categorical Data Clustering Ensemble Methods in
DataMining” International Journal of Computer Applications Vol 81,
No.4 November 2013.
@article{"International Journal of Information, Control and Computer Sciences:68015", author = "S. Sarumathi and N. Shanthi and S. Vidhya and M. Sharmila", title = "A Comprehensive Review on Different Mixed Data Clustering Ensemble Methods", abstract = "An extensive amount of work has been done in data
clustering research under the unsupervised learning technique in Data
Mining during the past two decades. Moreover, several approaches
and methods have been emerged focusing on clustering diverse data
types, features of cluster models and similarity rates of clusters.
However, none of the single clustering algorithm exemplifies its best
nature in extracting efficient clusters. Consequently, in order to
rectify this issue, a new challenging technique called Cluster
Ensemble method was bloomed. This new approach tends to be the
alternative method for the cluster analysis problem. The main
objective of the Cluster Ensemble is to aggregate the diverse
clustering solutions in such a way to attain accuracy and also to
improve the eminence the individual clustering algorithms. Due to
the massive and rapid development of new methods in the globe of
data mining, it is highly mandatory to scrutinize a vital analysis of
existing techniques and the future novelty. This paper shows the
comparative analysis of different cluster ensemble methods along
with their methodologies and salient features. Henceforth this
unambiguous analysis will be very useful for the society of clustering
experts and also helps in deciding the most appropriate one to resolve
the problem in hand.
", keywords = "Clustering, Cluster Ensemble Methods, Coassociation
matrix, Consensus Function, Median Partition.", volume = "8", number = "8", pages = "1493-11", }
