A Graph Based Approach for Clustering Ensemble of Fuzzy Partitions

Volume 6, Issue 2, pp 154 - 165 http://dx.doi.org/10.22436/jmcs.06.02.08

Download PDF

Download XML

• Export citations
  • CITATIONS & REFERENCES
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)
  • ARTICLE CITATION
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)
  • REFERENCES
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)

• 2923 Downloads
• 4746 Views

Authors

Mohammad Ahmadzadeh - Mazandaran University of Science and Technology. Zahra Azartash Golestan - Mazandaran University of Science and Technology. Javad Vahidi - Iran University of Science and Technology. Babak Shirazi - Mazandaran University of Science and Technology.

Abstract

Fuzzy clustering and Cluster Ensemble are important subjects in data mining. In recent years, fuzzy clustering algorithms have been growing rapidly, but fuzzy Clustering ensemble techniques have not grown much and most of them have been created by converting them to a fuzzy version of Consensus Function. In this paper, a fuzzy cluster ensemble method based on graph is introduced. Proposed approach uses membership matrixes obtained from multiple fuzzy partitions resulted by various fuzzy methods, and then creates fuzzy co-association matrixes for each partition which their entries present degree of correlation between related data points. Finally all of these matrixes summarize in another matrix called strength matrix and the final result is specified by an iterative decreasing process until one gets the desired number of clusters. Also a few data sets and some UCI datasets data set are used for evaluation of proposed methods. The proposed approach shows this could be more effective than base clustering algorithms same of FCM, K-means and spectral method and in comparison with various cluster ensemble methods, the proposed methods consist of results that are more reliable and less error rates than other methods.

Share and Cite

Keywords

• Fuzzy Clustering Ensemble
• Fuzzy Co-association Matrix
• Dissimilarity Matrix.

MSC

•  68P15
•  68P20
•  68T05

References

• [1] A. K. Jain, M. N. Murty, P. J. Flynn, Data clustering: a review, ACM Computing Surveys , 31 (1999), 264–323.
  • View Article
  • Google Scholar


• [2] H. Alizadeh, Clustering Ensemble Based on a Subset of Primary Results, Master of Science Thesis , Iran University of Science and Technology (2009)
  • Google Scholar


• [3] X. Z. Fern, W. Lin, Cluster ensemble selection, Stat. Anal. Data Min., 1(3) (2008), 128-141.
  • View Article
  • Google Scholar


• [4] Y. Hong, S. Kwong, H. Wang, Q. Ren, Resembling-based selective clustering ensembles, Pattern Recognition Letter , 30(3) (2009), 298-305.
  • View Article
  • Google Scholar


• [5] J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York (1981)
  • MathSciNet


• [6] F. Hoppner, F. Klawonn, R. Kruse, T. Runkler, Fuzzy Cluster Analysis, Wiley Press, Chichester (1999)
  • Google Scholar


• [7] A. Fred, A. K. Jain, Learning Pairwise Similarity for Data Clustering, In Proc. Of the 18th Int. Conf. on Pattern Recognition (ICPR'06), Hong Kong, China (2006)
  • View Article
  • Google Scholar


• [8] J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques, Third Edition Elsevier Inc , (2012)
  • Google Scholar
  • MATH


• [9] R. J. G. B. Campello, Generalized external indexes for comparing data partitions with overlapping Categories, Pattern Recognition Letters , 31 (2010), 966–975.
  • View Article
  • Google Scholar


• [10] R. J. G. B. Campello, A fuzzy extension of the Rand index and other related indexes for clustering and Classification assessment, Pattern Recognition Letters, 28 (2007), 833–841.
  • View Article
  • Google Scholar


• [11] F. Kovács, C. Legány, A. Babos, Cluster Validity Measurement Techniques, in: Proceedings of the fifth WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and DataBases, Madrid, Spain, (2006), 388–393.
  • Google Scholar


• [12] A. K. Das, J. Sil , Cluster Validation using Splitting and Merging Technique, in proc. of Int. Conf. on Computational Intelligence and Multimedia Applications, ICCIMA (2007)
  • View Article
  • Google Scholar


• [13] A. L. N. Fred, A. K. Jain, Combining multiple clusterings using evidence accumulation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (2005), 835–850.
  • View Article
  • Google Scholar


• [14] A. Strehl, J. Ghosh, Cluster ensembles: a knowledge reuse framework for combining multiple partitions, Journal of Machine Learning Research , 3 (2002), 583–617.
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [15] S. Vega-Pons, J. Correa-Morris, J. Ruiz-Shulcloper, Weighted cluster ensemble using a kernel consensus function, in: J. Ruiz-Shulcloper, W.G. Kropatsch (Eds.), CIARP , Lecture Notes in Computer Science, 5197 (2008), 195–202.
  • View Article
  • Google Scholar


• [16] S. Vega-Pons, J. Ruiz-Shulcloper, A survey of clustering ensemble algorithms, International Journal of Pattern Recognition and Artificial Intelligence , 25 (2011), 337-372.
  • View Article
  • MathSciNet
  • Google Scholar


• [17] J. Liu, M. Xu, Kernelized fuzzy attribute C-means clustering algorithm, Fuzzy Sets and Systems , 159 (2008), 2428 – 2445.
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [18] P. Y. Mok, H. Q. Huang, Y. L. Kwok, J. S. Au , A robust adaptive clustering analysis method for automatic Identification of clusters, Pattern Recognition , 45 (2012), 3017–3033.
  • View Article
  • Google Scholar


• [19] , , <http://archive.ics.uci.edu>, ()
  • Google Scholar


• [20] M. Filippone, F. Camastra, F. Masulli, S. Rovetta, A survey of kernel and spectral methods for clustering, Pattern Recognition, 41 (2008), 176–190.
  • View Article
  • Google Scholar
  • MATH


• [21] J. Shi, J. Malik, Normalized cuts and image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence , 22 (8) (2000), 888–905.
  • View Article
  • Google Scholar