A Survey on K-means Based Consensus Clustering

Nimesha M. Patil, Dipak V. Patil


Cluster ensemble techniques aim at combining multiple
individual clustering solutions into a consensus one, which agrees as
much as possible with existing individual clustering solutions.
These individual clustering solutions may be heterogeneous in
nature as they are obtained by multiple runs of different clustering
algorithms or multiple runs of same algorithm with dynamic
variable settings on the same dataset. There are two import key
issues in designing methodology for consensus clustering problem to
work on heterogeneous partitions. One is availability of good
consensus function that fixes the consensus partition by verifying
utilities of available existing clustered partitions. Another is use of
the efficient suitable clustering methodology to fit into consensus
clustering like k-means algorithm. K-means based consensus
clustering (KCC) is one of the prominent solution studied in recent
years of research. Hence, our survey here tries to cover recent and
major advances in k-means and consensus clustering separately.
Different approaches used by researchers to improve the results of
both paradigms individually are promising. Picking up innovative
solutions from both k-means and consensus clustering can build
better integrated and sophisticated KCC frameworks. This survey
can give better future directions for improving clustering quality in
heterogeneous environments through the means of KCC.

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T. Li, M. M. Ogihara, and S. Ma, “On combining multiple clusterings: an

overview and a new perspective,” Appl. Intell., vol. 32, no. 2, pp. 207–219,

A. Goder and V. Filkov, “Consensus clustering algorithms: Comparison and

refinement,” in Proc. SIAM Workshop Algorithm Eng. Exp., pp. 109–117,

Iam-On, N. and Boongoen, T., “Improved link-based cluster ensembles,”

Proceeding of International Joint Conference on Neural Networks (IJCNN), pp.

-8, 2012.

Tsaipei Wang, “CA-Tree: A Hierarchical Structure for Efficient and Scalable

Coassociation-Based Cluster Ensembles,” IEEE Transactions on Systems, Man,

and Cybernetics, Part B: Cyberneticsm, pp. 686 – 698, Volume-41, Issue-3,

Tapas Kanungo and Nathan S. Netanyahu, “An Efficient k-Means Clustering

Algorithm: Analysis and Implementation,” IEEE Transactions on Pattern

Analysis and Machine Intelligence, Vol. 24, No. 7, 2002.

NuwanGanganath and Chi-Tsun Cheng, “Data Clustering with Cluster Size

Constraints Using a Modified k-means Algorithm,” International Conference on

Cyber-Enabled Distributed Computing and Knowledge Discovery, 2014.

NavjotKaur, JaspreetKaurSahiwal, NavneetKaur ,“Efficient K-Means

Clustering algorithm Using Ranking Method In Data Mining” International

Journal of Advance Research in Computer Engineering & Technology ,Volume

, Issue 3, May2012.

Bernard J. Jansen, Danielle L. Booth Amanda Spink “Determining the User

Intent of Web Search Engine Queries,” Proceeding of 16th International

Conference on World Wide Web, pp. 1149-1150, 2007.

Shi Na, Liu Xumin and Guan Yong, “Research on k-means clustering

algorithm: An improved K-means Clustering Algorithm,” Proceeding of 3rd

IEEE International Symposium on Intelligent Information Technology and

Security Informatics (IITSI), pp. 63-67, 2010.

Iam-On, N. and Boongoen, T., “Improved link-based cluster ensembles,”

Proceeding of International Joint Conference on Neural Networks (IJCNN), pp.

-8, 2012.

Chen-Chung Liu and Shao-Wei Chu, “A Modified K-means Algorithm -

Two-Layer K-means Algorithm,” Tenth International Conference on Intelligent

Information Hiding and Multimedia Signal Processing, 2014.

Bhatia S., “New Improved technique for initial cluster centers of k-means

clustering using Genetic Algorithm,” Proceeding of IEEE 3International

Conference for Convergence of Technology (I2CT), pp. 1-4, 2014.

Jiejhang and Jianrui Dong, “A new method on finding optimal centers for

improving k-means algorithm,” Proceeding of 27th IEEE Control and Decision

Conference (CCDC), pp-1827-1832, May, 2015.

Juntaowang and Xiaolong Su, “An improved K-means clustering

algorithm,” Proceeding of IEEE 3rd International Conference on

Communication Software and Networks (ICCSN), pp-44-46, 2011.

J. Wu, H. Xiong, and J. Chen, “Adapting the right measures for k-means

clustering,” in Proc. 15th ACM SIGKDD Int. Conf. Knowl. Disc. Data Mining,

pp. 877–886, 2009.

Nisha, M.N ,Mohanavalli, S. and Swathika, R., “Improving the quality of

clustering using cluster ensembles,” Proceeding of IEEE Conference on

Information & Communication Technologies (ICT), pp. 88-92, 2013.

Hong Li, Hao Lin, Junjie Wu and Gong Cheng, “BV-RSA: A rapid

simulated annealing model for ensemble clustering,” Proceeding of 12th

International Conference on Service Systems and Service Management

(ICSSSM), pp. 1-6, 2015.

Barr J.R, Bowyer, K.W. and Flynn, P.J, “Framework for Active Clustering

With Ensembles,” IEEE Transactions on Information Forensics and Security,

Volume-9, Issue-11, 2014.

Hongjun Wang, Hanhuai Shan, Arindam Banerjee, “Bayesian Cluster

Ensembles,” SDM, pp. 209-220, 2009.

TommiJaakkola, Michael I. Jordan, “Variational Probabilistic Inference

and the QMR-DT Network,” J. Artif. Intell. Res. (JAIR) (JAIR) 10:291-


AnupamaChadha and Suresh Kumar, “An Improved K-Means Clustering

Algorithm: A Step Forward for Removal of Dependency on K,” 2014

International Conference on Reliability, Optimization and Information

Technology -ICROIT, India, 2014.

H. G. Ayad and M. S. Kamel, “Cumulative voting consensus method for

partitions with variable number of clusters,” IEEE Trans. Pattern Anal. Mach.

Intell., vol. 30, no. 1, pp. 160–173, Jan. 2008.

Shaohong Zhang, Liu Yang and DongqingXie, “Unsupervised evaluation of

cluster ensemble solutions,” Proceeding of IEEE 7th International Conference on

Advanced Computational Intelligence (ICACI), pp. 101 – 106, March 2015.

Carl Meyer, Shaina Race and Kevin Valakuzhy, “Determining the Number

of Clusters via Iterative Consensus Clustering,” August 6, 2014.

Sadeghian, A.H. and Nezamabadi-pour H., “Gravitational ensemble

clustering,” Proceeding of IEEE Iranian Conference on Intelligent Systems

(ICIS), pp. 1-6, 2014.

ShiYao Liu, Qi Kang, Jing An and MengChu Zhou, “A weightincorporated

similarity-based clustering ensemble method,” Proceeding of 2014

IEEE 11th International Conference on Networking, Sensing and Control

(ICNSC), pp. 719 – 724, 2014.

Abu-Jamous, B, RuiFa, Nandi A.K., Roberts D.J., “Binarization of

Consensus Partition Matrix for ensemble clustering,” Proceedings of the 20th

European Signal Processing Conference (EUSIPCO), pp. 2193 – 2197, 2012.

Jinfeng Yi, Tianbao Yang, Rong Jin and Jain, A.K., “Robust Ensemble

Clustering by Matrix Completion,” Proceeding of IEEE 12th International

Conference on Data Mining (ICDM), pp. 1176 – 118, 2012.


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