Geodesic
Consensus clustering with differential evolution
Kybernetika, Tome 50 (2014) no. 5, pp. 661-678
Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

Voir la notice de l'article

Consensus clustering algorithms are used to improve properties of traditional clustering methods, especially their accuracy and robustness. In this article, we introduce our approach that is based on a refinement of the set of initial partitions and uses differential evolution algorithm in order to find the most valid solution. Properties of the algorithm are demonstrated on four benchmark datasets.
Consensus clustering algorithms are used to improve properties of traditional clustering methods, especially their accuracy and robustness. In this article, we introduce our approach that is based on a refinement of the set of initial partitions and uses differential evolution algorithm in order to find the most valid solution. Properties of the algorithm are demonstrated on four benchmark datasets.
DOI : 10.14736/kyb-2014-5-0661
Classification : 62H30, 92G30
Keywords: consensus clustering; differential evolution; ensemble; data 
@article{10_14736_kyb_2014_5_0661,
     author = {Sabo, Miroslav},
     title = {Consensus clustering with differential evolution},
     journal = {Kybernetika},
     pages = {661--678},
     year = {2014},
     volume = {50},
     number = {5},
     doi = {10.14736/kyb-2014-5-0661},
     mrnumber = {3301853},
     zbl = {1308.62132},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.14736/kyb-2014-5-0661/}
}
TY  - JOUR
AU  - Sabo, Miroslav
TI  - Consensus clustering with differential evolution
JO  - Kybernetika
PY  - 2014
SP  - 661
EP  - 678
VL  - 50
IS  - 5
UR  - http://geodesic.mathdoc.fr/articles/10.14736/kyb-2014-5-0661/
DO  - 10.14736/kyb-2014-5-0661
LA  - en
ID  - 10_14736_kyb_2014_5_0661
ER  - 
%0 Journal Article
%A Sabo, Miroslav
%T Consensus clustering with differential evolution
%J Kybernetika
%D 2014
%P 661-678
%V 50
%N 5
%U http://geodesic.mathdoc.fr/articles/10.14736/kyb-2014-5-0661/
%R 10.14736/kyb-2014-5-0661
%G en
%F 10_14736_kyb_2014_5_0661
Sabo, Miroslav. Consensus clustering with differential evolution. Kybernetika, Tome 50 (2014) no. 5, pp. 661-678. doi: 10.14736/kyb-2014-5-0661

[1] Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications. In: Proc. 2001 ACM SIGMOD International Conference on Management of data 27 (1998), 2, pp. 94-105.

[2] Bache, K., Lichman, M.: UCI machine learning repository, 2013. URL http://archive.ics.uci.edu/ml

[3] Bailey, K. D.: Typologies and Taxonomies: An Introduction to Classification Techniques. Sage Publications Inc., Los Angeles 1994.

[4] Bezdek, J. C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York 1981. | MR | Zbl

[5] Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Sys. Man Cyber., Part A: Systems and Humans 38 (2008), 1, 218-237. | DOI

[6] Dempster, A. P., Laird, N. M., Rubin, D. B.: Maximum likelihood from incomplete data via the em algorithm. J. Roy. Stat. Soc. Ser. B 39 (1977), 1, 1-38. | MR | Zbl

[7] Dimitriadou, E.: cclust: Convex Clustering Methods and Clustering Indexes, 2012. URL http://CRAN.R-project.org/package=cclust

[8] Dudoit, S., Fridlyand, J.: Bagging to improve the accuracy of a clustering procedure. Bioinformatics 19 (2003), 9, 1090-2003. | DOI

[9] Ester, M., Kriegel, H. P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. 2nd International Conference on Knowledge Discovery and Data Mining 1996, pp. 226-231.

[10] Fern, X., Brodley, C.: Solving cluster ensemble problems by bipartite graph partitioning. In: Proc. 21st International Conference on Machine learning 2004, pp. 36-43.

[11] Fraley, C., Raftery, A. E.: Model-based clustering, discriminant analysis and density estimation. J. Amer. Statist. Assoc. 97 (2002), 611-631. | DOI | MR | Zbl

[12] Fraley, C., Raftery, A. E.: MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering. Techn. Report 504, University of Washington, Department of Statistics, 2006.

[13] Ghaemi, R., Sulaiman, N., Ibrahim, H., Mustapha, N.: A survey: Clustering ensembles techniques. In: Proc. International Conference on Computer, Electrical, and Systems Science, and Engineering (CESSE) 38 (2009), pp. 644-653.

[14] Ghosh, J., Acharya, A.: Cluster ensembles. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery 1 (2011), 4, 305-315.

[15] Gould, S. J.: Full House: The Spread of Excellence from Plato to Darwin. Harmony, New York 1996.

[16] Halkidi, M., Batistakis, Y., Vazirgiannis, M.: Cluster validity methods: Part i. SIGMOD Record 31 (2002), 2, 40-45. | DOI

[17] Handl, J., Knowles, J.: Multi-objective clustering and cluster validation. In: Multi-Objective Machine Learning (Studies in Computational Intelligence, Vol, 16), Springer, Berlin 2006, pp. 21-47.

[18] Handl, J., Knowles, J.: An evolutionary approach to multiobjective clustering. IEEE Trans. Evolutionary Comput. 11 (2007), 56-76. | DOI

[19] Handl, J., Knowles, J., Kell, D.: Computational cluster validation in post-genomic data analysis. Bioinformatics 21 (2005), 15, 3201-3212. | DOI

[20] Hartigan, J., Wong, M.: A k-means clustering algorithm. Applied Statistics 28 (1979), 100-108. | DOI | Zbl

[21] Hornik, K., Feinerer, I., Kober, M., Buchta, C.: Spherical $k$-means clustering. J. Statist. Software 50 (2012), 10, 1-22. | DOI

[22] Hruschka, E., Campello, R., Freitas, A., Carvalho, A. de: A survey of evolutionary algorithms for clustering. IEEE Trans. Sys. Man Cyber. Part C: Applications and Reviews 39 (2009), 2, 133-155. | DOI

[23] Jain, A. K.: Data clustering: 50 years beyond k-means. Pattern Recognition Lett. 31 (2010), 8, 651-666.

[24] Jain, A. K., Murty, M. N., Flynn, P. J.: Data clustering: A review. ACM Comput. Surveys 31 (1999), 3, 316-323.

[25] Karatzoglou, A., Smola, A., Hornik, K., Zeileis, A.: kernlab - an S4 package for kernel methods in R. J. Statist. Software 11 (2004), 9, 1-20.

[26] Karypis, G., Aggarwal, R., Kumar, V., Shekhar, S.: Multilevel hypergraph partitioning: Applications in vlsi domain. In: Proc. Design and Automation Conference, 1997, pp. 526-529.

[27] Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York 1990. | MR

[28] Krishna, K., Murty, M. Narasimha: Genetic k-means algorithm. Trans. Sys. Man Cyber. Part B 29 (1999), 3, 433-439. | DOI

[29] Kwedlo, W.: A clustering method combining differential evolution with the k-means algorithm. Pattern Recognition Letters 32 (2011), 12, 1613-1621. | DOI

[30] MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proc. Fifth Berkeley Symposium on Mathematical Statistics and Probability 1 (1967), pp. 281-297. | MR | Zbl

[31] Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K.: cluster: Cluster Analysis Basics and Extensions, 2013. R package version 1.14.4.

[32] Monti, S., Tamayo, P., Mesirov, J., Golub, T.: Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn. 52 (2003), 1-2, 91-118. | DOI | Zbl

[33] Mullen, K., Ardia, D., Gil, D., Windover, D., Cline, J.: DEoptim: An R package for global optimization by differential evolution. J. Statist. Software 40 (2011), 6, 1-26. | DOI

[34] Murthy, C., Chowdhury, N.: In search of optimal clusters using genetic algorithms. Pattern Recognition Lett. 17 (1996), 8, 825-832.

[35] Pal, S. K., Majumder, D. D.: Fuzzy sets and decision making approaches in vowel and speaker recognition. IEEE Trans. Sys. Man Cyber. 7 (1977), 625-629. | DOI

[36] Paterlini, S., Krink, T.: Differential evolution and particle swarm optimisation in partitional clustering. Comput. Statist. Data Anal. 50 (2006), 5, 1220-1247. | DOI | MR

[37] Price, K. V., Storn, R. M., Lampinen, J. A.: Differential Evolution: A Practical Approach to Global Optimization. Springer-Verlag, Berlin 2006. | MR | Zbl

[38] Raghavan, V., Birchand, K.: A clustering strategy based on a formalism of the reproductive process in a natural system. In: Proc. Second International Conference on Information Storage and Retrieval, 1979, pp. 10-22.

[39] R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna 2012. URL http://www.R-project.org/

[40] Shi, J., Malik, J.: Normalized cuts and image segmentation. In: IEEE Trans. Pattern Analysis and Machine Intelligence 22 (2000), 8, 888-905.

[41] Simovici, D. A., Djeraba, Ch.: Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics. Advanced information and knowledge processing. Springer, London 2008. | MR | Zbl

[42] Simpson, T. I., Armstrong, J. D., Jarman, A. P.: Merged consensus clustering to assess and improve class discovery with microarray data. BMC Bioinform. 11 (2010), 11-590. | DOI

[43] Sneath, P. H.: The application of computers to taxonomy. Journal of general microbiology 17 (1957), 1, 201-226. | DOI

[44] Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11 (1997), 4, 341-359. | DOI | MR | Zbl

[45] Strehl, A., Ghosh, J.: Cluster ensembles - a knowledge reuse framework for combining partitionings. In: Proc. 11th National Conference On Artificial Intelligence, NCAI, Edmonton, Alberta 2002, pp. 93-98. | MR

[46] Topchy, A., Jain, A., Punch, W.: A mixture model of clustering ensembles. In: Proc. SIAM International Conference on Data Mining 2004, pp. 22-24.

[47] Trotter, W. M.: Combinatorics and Partially Ordered Sets. The Johns Hopkins University Press, Baltimore 1992. | MR | Zbl

[48] Tvrdík, J., Křivý, I.: Differential evolution with competing strategies applied to partitional clustering. Lecture Notes Comput. Sci. 7269 (2012), 136-144. | DOI

[49] Wang, P., Domeniconi, C., Laskey, K.: Nonparametric bayesian clustering ensembles. Lecture Notes Comput. Sci. 6323 (2010), 3, 435-450. | DOI

[50] Wang, H., Shan, H., Banerjee, A.: Bayesian cluster ensembles. Stat. Anal. Data Min. 4 (2011), 1, 54-70. | DOI | MR

[51] Wikipedia: Partition of a set. http://en.wikipedia.org/wiki/Partition_of_a_set

[52] Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Networks 16 (2005), 3, 645-678. | DOI

[53] Zahn, Ch. T.: Graph-theoretic methods for detecting and describing gestalt clusters. IEEE Trans. Comput. 20 (1971), 31, 68-86.

Cité par Sources :