research-article

A survey of evolutionary algorithms for clustering

Authors:

Eduardo Raul Hruschka,

Ricardo J. G. B. Campello,

Alex A. Freitas,

André C. Ponce Leon F. De CarvalhoAuthors Info & Claims

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Volume 39, Issue 2

Pages 133 - 155

https://doi.org/10.1109/TSMCC.2008.2007252

Published: 01 March 2009 Publication History

Abstract

This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context, most of the paper is devoted to partitional algorithms that look for hard clusterings of data, though overlapping (i.e., soft and fuzzy) approaches are also covered in the paper. The paper is original in what concerns two main aspects. First, it provides an up-to-date overview that is fully devoted to evolutionary algorithms for clustering, is not limited to any particular kind of evolutionary approach, and comprises advanced topics like multiobjective and ensemble-based evolutionary clustering. Second, it provides a taxonomy that highlights some very important aspects in the context of evolutionary data clustering, namely, fixed or variable number of clusters, cluster-oriented or nonoriented operators, context-sensitive or context-insensitive operators, guided or unguided operators, binary, integer, or real encodings, centroid-based, medoid-based, label-based, tree-based, or graph-based representations, among others. A number of references are provided that describe applications of evolutionary algorithms for clustering in different domains, such as image processing, computer security, and bioinformatics. The paper ends by addressing some important issues and open questions that can be subject of future research.

References

[1]

V. S. Alves, R. J. G. B. Campello, and E. R. Hruschka, "A fuzzy variant of an evolutionary algorithm for clustering," in Proc. IEEE Int. Conf. Fuzzy Syst., 2007, pp. 375-380.

[2]

V. S. Alves, R. J. G. B. Campello, and E. R. Hruschka, "Towards a fast evolutionary algorithm for clustering," in Proc. IEEE Congr. Evol. Comput., 2006, pp. 6240-6247.

[3]

L. J. Arabie, G. Hubert, and P. DeSoete, Clustering and Classification. Singapore: World Scientific, 1999.

[4]

G. P. Babu and M. N. Murty, "Clustering with evolution strategies," Pattern Recognit., vol. 27, pp. 321-329, 1994.

[5]

R. Babuška, Fuzzy Modeling for Control. Norwell, MA: Kluwer, 1998.

Digital Library

[6]

T. Bäck, D. B. Fogel, and Z. Michalewicz, Evolutionary Computation 1: Basic Algorithms and Operators. Bristol, U.K.: Inst. Phys. Publishing (IOP), 2000.

[7]

P. Baldi and S. Brunak, Bioinformatics--The Machine Learning Approach, 2nd ed. Cambridge, MA: MIT Press, 2001.

Digital Library

[8]

S. Bandyopadhyay, U. Maulik, and A. Mukhopadhyay, "Multiobjective genetic clustering for pixel classification in remote sensing imagery," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 5, pp. 1506-1511, May 2007.

[9]

S. Bandyopadhyay, A. Mukhopadhyay, and U. Maulik, "An improved algorithm for clustering gene expression data," Bioinformatics, vol. 23, no. 21, pp. 2859-2865, 2007.

Digital Library

[10]

S. Bandyopadhyay and U. Maulik, "An evolutionary technique based on k-means algorithm for optimal clustering in R^N," Inf. Sci., vol. 146, pp. 221-237, 2002.

Digital Library

[11]

S. Bandyopadhyay and U. Maulik, "Genetic clustering for automatic evolution of clusters and application to image classification," Pattern Recognit., vol. 35, pp. 1197-1208, 2002.

[12]

S. Bandyopadhyay and U. Maulik, "Nonparametric genetic clustering: Comparison of validity indices," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 31, no. 1, pp. 120-125, Feb. 2001.

Digital Library

[13]

P. M. BertoneGerstein, "Integrative data mining: The new direction in bioinformatics--Machine learning for analyzing genome-wide expression profiles," IEEE Eng. Med. Biol. Mag., vol. 20, no. 4, pp. 33-40, Jul./Aug. 2001.

[14]

J. C. Bezdek, S. Boggavaparu, L. O. Hall, and A. Bensaid, "Genetic algorithm guided clustering," in Proc. IEEE Congr. Evol. Comput., 1994, pp. 34-40.

[15]

J. C. Bezdek and R. J. Hathaway, "Optimization of fuzzy clustering criteria using genetic algorithms," in Proc. IEEE World Congr. Comput. Intell., 1994, pp. 589-594.

[16]

J. C. Bezdek, Pattern Recognition With Fuzzy Objective Function Algorithm. New York: Plenum, 1981.

Digital Library

[17]

J. P. Bigus, Data Mining With Neural Networks. New York: McGraw-Hill, 1996.

Digital Library

[18]

R. B. Calinski and J. Harabasz, "A dendrite method for cluster analysis," Commun. Stat., vol. 3, pp. 1-27, 1974.

[19]

R. J. G. B. Campello, V. S. Alves, and E. R. Hruschka, "On the efficiency of evolutionary fuzzy clustering," J. Heuristics.

Digital Library

[20]

R. J. G. B. Campello and E. R. Hruschka, "A fuzzy extension of the Silhouette width criterion for cluster analysis," Fuzzy Sets Syst., vol. 157, no. 21, pp. 2858-2875, 2006.

[21]

A. Casillas, M. Y. G. de Lena, and R. Martínez, "Document clustering into an unknown number of clusters using a genetic algorithm," in Proc. Int. Conf. Text Speech Dialogue. Lecture Notes in Computer Science 2807, 2003, pp. 43-49.

[22]

C. A. C. Coello, D. A. V. Veldhuizen, and G. B. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems. Norwell, MA: Kluwer, 2002.

Digital Library

[23]

R. M. Cole, "Clustering with genetic algorithms," M.S. thesis, Univ. Western Australia, Perth, W.A., 1998.

[24]

T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, 2nd ed. Cambridge, MA: MIT Press, 2001.

Digital Library

[25]

D. W. Corne, N. R. Jerram, J. D. Knowles, and M. J. Oates, "PESA-II: Region-based selection in evolutionary multiobjective optimization," in Proc. Genetic Evol. Comput. Conf., 2001, pp. 283-290.

[26]

M. C. Cowgill, R. J. Harvey, and L. T. Watson, "A genetic algorithm approach to cluster analysis," Comput. Math. Appl., vol. 37, pp. 99-108, 1999.

[27]

S. Das, A. Abraham, and A. Konar, "Automatic clustering using an improved differential evolution algorithm," IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 38, no. 1, pp. 218-237, Jan. 2008.

Digital Library

[28]

D. L. Davies and D. W. Bouldin, "A cluster separation measure," IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-1, no. 4, pp. 224-227, Apr. 1979.

Digital Library

[29]

L. N. de Castro, E. R. Hruschka, and R. J. G. B. Campello, "An evolutionary clustering technique with local search to design RBF neural network classifiers," in Proc. IEEE Int. Conf. Neural Netw., 2004, pp. 2083-2088.

[30]

K. A. de Jong, Evolutionary Computation: A Unified Approach. Cambridge, MA: MIT Press, 2006.

[31]

C. S. de Oliveira, A. S. G. Meiguins, B. S. Meiguins, P. I. Godinho, and A. A. Freitas, "An evolutionary density and grid-based clustering algorithm," in Proc. XXIII Brazilian Symp. Databases (SBBD-2007), pp. 175-189.

[32]

J. V. de Oliveira and W. Pedrycz, Advances in Fuzzy Clustering and Its Applications. New York: Wiley, 2007.

Digital Library

[33]

K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms. New York: Wiley, 2001.

Digital Library

[34]

A. P. Dempster, N. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Stat. Soc., vol. B39, pp. 1- 38, 1977.

[35]

M. Dolled-Filhart, L. Ryden, M. Cregger, K. Jirstrom, M. Harigopal, R. L. Camp, and D. L. Rimm, "Classification of breast cancer using genetic algorithms and tissue microarrays," Clin. Cancer Res., vol. 12, pp. 6459-6468, 2006.

[36]

J. C. Dunn, "A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters," J. Cybern., vol. 3, pp. 32- 57, 1973.

[37]

M. A. Egan, M. Krishnamoorthy, and K. Rajan, "Comparative study of a genetic fuzzy C-means algorithm and a validity guided fuzzy C-means algorithm for locating clusters in noisy data," in Proc. IEEE World Congr. Comput. Intell., 1998, pp. 440-445.

[38]

M. Ester, H.-P. Kriegel, J. Xu, and W. Sander, "A density-based algorithm for discovering clusters in large spatial databases with noise," in Proc. 2nd Int. Conf. Knowl. Discovery Data Mining (KDD-2006), pp. 226-231.

[39]

V. Estivill-Castro and A. T. Murray, "Spatial clustering for data mining with genetic algorithms," in Proc. Int. ICSC Symp. Eng. Intell. Syst., 1997, pp. 317-323.

[40]

B. S. Everitt, S. Landau, and M. Leese, Cluster Analysis. London, U.K.: Arnold, 2001.

Digital Library

[41]

K. Faceli, A. C. P. L. F. de Carvalho, and M. C. P. Souto, "Cluster ensemble and multi-objective clustering methods," in Pattern Recognition Technologies and Applications: Recent Advances, B. Verma and M. Blumenstein, Eds. Hershey, PA: Idea Group, 2008, pp. 325-343.

[42]

K. Faceli, A. C. P. L. F. de Carvalho, and M. C. P. Souto, "Multi-objective clustering ensemble," Int. J. Hybrid Intell. Syst., vol. 4, no. 3, pp. 145- 156, 2007.

Digital Library

[43]

E. Falkenauer, Genetic Algorithms and Grouping Problems. New York: Wiley, 1998.

Digital Library

[44]

P. Fazendeiro and J. V. de Oliveira, "A semantic driven evolutive fuzzy clustering algorithm," in Proc. IEEE Int. Conf. Fuzzy Syst., 2007, pp. 1-6.

[45]

D. B. Fogel, Evolutionary Computation: Principles and Practice for Signal Processing. Bellingham, WA: SPIE, 2000.

[46]

D. B. Fogel and P. K. Simpson, "Evolving fuzzy clusters," in Proc. IEEE Int. Conf. Neural Netw., 1993, pp. 1829-1834.

[47]

C. Fralley and A. E. Raftery, "How many clusters? Which clustering method? Answer via model-based cluster analysis," Comput. J., vol. 41, pp. 578-588, 1998.

[48]

P. Fränti, J. Kivijärvi, T. Kaukoranta, and O. Nevalainen, "Genetic algorithms for large-scale clustering problems," Comput. J., vol. 40, pp. 547- 554, 1997.

[49]

A. L. N. Fred and A. K. Jain, "Combining multiple clusterings using evidence accumulation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 6, pp. 835-850, Jun. 2005.

Digital Library

[50]

A. A. Freitas, "A review of evolutionary algorithms for data mining," in Soft Computing for Knowledge Discovery and Data Mining, O. Maimon and L. Rokach, Eds. New York: Springer-Verlag, 2007, pp. 61-93.

[51]

J. Ghosh, A. Strehl, and S. Merugu, "A consensus framework for integrating distributed clusterings under limited knowledge sharing," in Proc. NSF Workshop Next Generation Data Mining, Baltimore, MD, Nov. 2002, pp. 99-108.

[52]

A. Ghozeil and D. B. Fogel, "Discovering patterns in spatial data using evolutionary programming," in Proc. 1st Annu. Conf. Genetic Program., 1996, pp. 521-527.

Digital Library

[53]

F. Glover, "Tabu search: Part II," ORSA J. Comput., vol. 2, no. 1, pp. 4- 32, 1990.

[54]

D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley, 1989.

Digital Library

[55]

M. Halkidi, Y. Batistakis, and M. Vazirgiannis, "On clustering validation techniques," J. Intell. Inf. Syst., vol. 17, pp. 107-145, 2001.

Digital Library

[56]

L. O. Hall, J. C. Bezdek, S. Boggavarpu, and A. Bensaid, "Genetic fuzzy clustering," in Proc. Annu. Conf. North Amer. Fuzzy Inf. Process. Soc. (NAFIPS), 1994, pp. 411-415.

[57]

L. O. Hall and B. Özyurt, "Scaling genetically guided fuzzy clustering," in Proc. Int. Symp. Uncertainty Model Anal. Annu. Conf. North Amer. Fuzzy Inf. Process. Soc. (ISUMA-NAFIPS), 1995, pp. 328- 332.

Digital Library

[58]

L. O. Hall, I. B. Özyurt, and J. C. Bezdek, "Clustering with a genetically optimized approach," IEEE Trans. Evol. Comput., vol. 3, no. 2, pp. 103- 112, Jul. 1999.

Digital Library

[59]

J. Handl and J. Knowles, "An evolutionary approach to multiobjective clustering," IEEE Trans. Evol. Comput., vol. 11, no. 1, pp. 56-76, Feb. 2007.

Digital Library

[60]

J. Handl, J. Knowles, and D. B. Kell, "Computational cluster validation in post-genomic data analysis," Bioinformatics, vol. 21, pp. 3201-3212, 2005.

Digital Library

[61]

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer-Verlag, 2001.

[62]

S. Haykin, Neural Networks--A Comprehensive Foundation, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 1999.

Digital Library

[63]

J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor, MI: Univ. Michigan Press, 1975.

Digital Library

[64]

F. Höppner, F. Klawonn, R. Kruse, and T. Runkler, Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. New York: Wiley, 1999.

[65]

E. R. Hruschka and N. F. F. Ebecken, "A genetic algorithm for cluster analysis," Intell. Data Anal., vol. 7, pp. 15-25, 2003.

Digital Library

[66]

E. R. Hruschka, R. J. G. B. Campello, and L. N. de Castro, "Clustering gene-expression data: A hybrid approach that iterates between k-means and evolutionary search," in Hybrid Evolutionary Algorithms, C. Grosan, A. Abraham, and H. Ishibuchi, Eds. New York: Springer-Verlag, 2007, pp. 313-335.

[67]

E. R. Hruschka, R. J. G. B Campello, and L. N. de Castro, "Evolutionary search for optimal fuzzy C-means clustering," in Proc. Int. Conf. Fuzzy Syst., 2004, pp. 685-690.

[68]

E. R. Hruschka, R. J. G. B. Campello, and L. N. de Castro, "Evolving clusters in gene-expression data," Inf. Sci., vol. 176, pp. 1898-1927, 2006.

Digital Library

[69]

E. R. Hruschka, R. J. G. B. Campello, and L. N. de Castro, "Improving the efficiency of a clustering genetic algorithm," in Proc. 9th Ibero-Amer. Conf. Artif. Intell. Lecture Notes in Computer Science, vol. 3315. Berlin, Germany: Springer-Verlag, 2004, pp. 861-870.

[70]

E. R. Hruschka, L. N. de Castro, and R. J. G. B. Campello, "Evolutionary algorithms for clustering gene-expression data," in Proc. 4th IEEE Int. Conf. Data Mining, 2004, pp. 403-406.

Digital Library

[71]

E. R. Hruschka and N. F. F. Ebecken, "Extracting rules from multilayer perceptrons in classification problems: A clustering-based approach," Neurocomputing, vol. 70, pp. 384-397, 2006.

[72]

A. K. Jain and R. C. Dubes, Algorithms for Clustering Data. Englewood Cliffs, NJ: Prentice-Hall, 1988.

Digital Library

[73]

A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: A review," ACM Comput. Surv., vol. 31, pp. 264-323, 1999.

Digital Library

[74]

D. Jiang, C. Tang, and A. Zhang, "Cluster analysis for gene expression data: A survey," IEEE Trans. Knowl. Data Eng., vol. 16, no. 11, pp. 1370-1386, Nov. 2004.

Digital Library

[75]

L. Kaufman and P. J. Rousseeuw, Finding Groups in Data--An Introduction to Cluster Analysis. Series in Probability and Mathematical Statistics. New York: Wiley, 1990.

[76]

P. Kellam, X. Liu, N. J. Martin, C. Orengo, S. Swift, and A. Tucker, "Comparing, contrasting and combining clusters in viral gene expression data," in Proc. 6th Workshop Intell. Data Anal. Med. Pharmacol., 2001, pp. 56-62.

[77]

H. Kim, G. H. Golub, and H. Park, "Missing value estimation for DNA microarray gene expression data: Local least squares imputation," Bioinformatics, vol. 21, pp. 187-198, 2005.

Digital Library

[78]

Y. Kim, W. N. Street, and F. Menczer, "Feature selection in unsupervised learning via evolutionary searching," in Proc. 6th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining (KDD 2000), 2000, pp. 365- 369.

Digital Library

[79]

J. Kivijärvi, P. Fränti, and O. Nevalainen, "Self-adaptive genetic algorithm for clustering," J. Heuristics, vol. 9, pp. 113-129, 2003.

Digital Library

[80]

F. Klawonn, "Fuzzy clustering with evolutionary algorithms," in Proc. 7th Int. Fuzzy Syst. Assoc. (IFSA) World Congr., 1997, pp. 312-323.

[81]

D. E. Knuth, The Art of Computing Programming, vol. III, Sorting and Searching. Reading, MA: Addison-Wesley, 1973.

Digital Library

[82]

E. E. Korkmaz, J. Du, R. Alhajj, and K. Barker, "Combining advantages of new chromosome representation scheme and multi-objective genetic algorithms for better clustering," Intell. Data Anal., vol. 10, no. 2, pp. 163-182, 2006.

Digital Library

[83]

K. Krishna and N. Murty, "Genetic K-means algorithm," IEEE Trans. Syst., Man Cybern. B, Cybern., vol. 29, no. 3, pp. 433-439, Jun. 1999.

Digital Library

[84]

R. Krovi, "Genetic algorithms for clustering: A preliminary investigation," in Proc. 25th Hawaii Int. Conf. Syst. Sci., 1992, vol. 4, pp. 540- 544.

[85]

L. I. Kuncheva and J. C. Bezdek, "Selection of cluster prototypes from data by a genetic algorithm," in Proc. 5th Eur. Congr. Intell. Tech. Soft Comput., 1997, pp. 1683-1688.

[86]

L. I. Kuncheva, S. T. Hadjitodorov, and L. P. Todorova, "Experimental comparison of cluster ensemble methods," in Proc. FUSION, 2006, pp. 105-115.

[87]

E. G. Lacerda, A. C. P. F. de Carvalho, A. P. Braga, and T. B. Ludermir, "Evolutionary radial basis functions for credit assessment," Appl. Intell., vol. 22, no. 3, pp. 167-181, May 2005.

Digital Library

[88]

E. G. Lacerda, A. C. P. F. de Carvalho, and T. B. Ludermir, "Evolutionary optimization of RBF networks, radial basis function neural networks: Design and applications," in Radial Basis Function Networks 1: Recent Developments in Theory and Application, R. J. Howlett and L. Jain, Eds. Heidelberg, Germany: Physica-Verlag, 2001, pp. 282-310.

Digital Library

[89]

H. Liu, J. Li, and M. A. Chapman, "Automated road extraction from satellite imagery using hybrid genetic algorithms and cluster analysis," J. Environ. Inf., vol. 1, no. 2, pp. 40-47, 2003.

[90]

H. Liu and L. Yu, "Toward integrating feature selection algorithms for classification and clustering," IEEE Trans. Knowl. Data Eng., vol. 17, no. 4, pp. 1-12, Apr. 2005.

Digital Library

[91]

J. Liu and W. Xie, "A genetics-based approach to fuzzy clustering," in Proc. Int. Conf. Fuzzy Syst., 1995, pp. 2233-2240.

[92]

Y. Liu, K. Chen, X. Liao, and W. Zhang, "A genetic clustering method for intrusion detection," Pattern Recognit., vol. 37, pp. 927-942, 2004.

[93]

J. A. Lozano and P. Larrañaga, "Applying genetic algorithms to search for the best hierarchical clustering of a dataset," Pattern Recognit. Lett., vol. 20, no. 9, pp. 911-918, 1999.

Digital Library

[94]

Y. Lu, S. Lu, F. Fotouhi, Y. Deng, and S. J. Brown, "FGKA: A fast genetic K-means clustering algorithm," in Proc. ACM Symp. Appl. Comput., 2004, pp. 622-623.

Digital Library

[95]

Y. Lu, S. Lu, F. Fotouhi, Y. Deng, and S. J. Brown, "Incremental genetic k-means algorithm and its application in gene expression data analysis," BMC Bioinf., vol. 5, pp. 1-10, 2004.

[96]

C. B. Lucasius, A. D. Dane, and G. Kateman, "On k-medoid clustering of large data sets with the aid of a genetic algorithm: Background, feasibility and comparison," Anol. Chim. Acta, vol. 282, pp. 647-669, 1993.

[97]

P. C. H. Ma, K. C. C. Chan, X. Yao, and D. K. Y. Chiu, "An evolutionary clustering algorithm for gene expression microarray data analysis," IEEE Trans. Evol. Comput., vol. 10, no. 3, pp. 296-314, Jun. 2006.

Digital Library

[98]

T. Martinez and K. Schulten, "Topology representing networks," Neural Netw., vol. 7, no. 3, pp. 507-522, 1994.

Digital Library

[99]

U. Maulik and S. Bandyopadhyay, "Fuzzy partitioning using real coded variable length genetic algorithm for pixel classification," IEEE Trans. Geosci. Remote Sens., vol. 41, no. 5, pp. 1075-1081, May 2003.

[100]

U. Maulik and S. Bandyopadhyay, "Genetic algorithm-based clustering technique," Pattern Recognit., vol. 33, pp. 1455-1465, 2000.

[101]

J. B. MacQueen, "Some methods of classification and analysis of multivariate observations," in Proc. 5th Berkeley Symp. Math. Stat. Probab., 1967, pp. 281-297.

[102]

G. Mecca, S. Raunich, and A. Pappalardo, "A new algorithm for clustering search results," Data Knowl. Eng., vol. 62, pp. 504-522, 2007.

Digital Library

[103]

P. Merz and A. Zell, "Clustering gene expression profiles with memetic algorithms," in Proc. Parallel Prob. Solving Nature. Lecture Notes in Computer Science, vol. 2439. Berlin, Germany: Springer-Verlag, 2002, pp. 811-820.

Digital Library

[104]

G. W. Milligan and M. C. Cooper, "An examination of procedures for determining the number of clusters in a data set," Psychometrika, vol. 50, pp. 159-179, 1985.

[105]

M. Mitchell, An Introduction to Genetic Algorithms. Cambridge, MA: MIT Press, 1998.

Digital Library

[106]

A. Mukhopadhyay, U. Maulik, and S. Bandyopadhyay, "Multiobjective genetic fuzzy clustering of categorical attributes," in Proc. 10th Int. Conf. Inf. Technol., 2007, pp. 74-79.

Digital Library

[107]

C. A. Murthy and N. Chowdhury, "In search of optimal clusters using genetic algorithms," Pattern Recognit. Lett., vol. 17, pp. 825-832, 1996.

Digital Library

[108]

M. C. Naldi and A. C. P. L. F. de Carvalho, "Clustering using genetic algorithm combining validation criteria," in Proc. 15th Eur. Symp. Artif. Neural Netw., Bruges, Belgium, 2007, pp. 139-147.

[109]

M. C. Naldi, A. C. P. L. F. de Carvalho, R. J. G. B. Campello, and E. R. Hruschka, "Genetic clustering for data mining," in Soft Computing for Knowledge Discovery and Data Mining, O. Maimon and L. Rokach, Eds. New York: Springer-Verlag, 2007, pp. 113-132.

[110]

M. Ouyang, W. J. Welsh, and P. Georgopoulos, "Gaussian mixture clustering and imputation of microarray data," Bioinformatics, vol. 20, pp. 917-923, 2004.

Digital Library

[111]

M. K. Pakhira, S. Bandyopadhyay, and U. Maulik, "A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification," Fuzzy Sets Syst., vol. 155, pp. 191-214, 2005.

Digital Library

[112]

N. R. Pal and J. C. Bezdek, "On cluster validity for the fuzzy C-means model," IEEE Trans. Fuzzy Syst., vol. 3, no. 3, pp. 370-379, Aug. 1995.

Digital Library

[113]

S. Pan and K. Cheng, "Evolution-based tabu search approach to automatic clustering," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 37, no. 5, pp. 827-838, Sep. 2007.

Digital Library

[114]

P. A. Pantel, "Clustering by committee," Ph.D. dissertation, Dept. Comput. Sci., Univ. Alberta, Edmonton, AB, Canada, 2003.

Digital Library

[115]

H.-S. Park, S.-H. Yoo, and S.-B. Cho, "Evolutionary fuzzy clustering algorithm with knowledge-based evaluation and applications for gene expression profiling," J. Comput. Theor. Nanosci., vol. 2, pp. 1-10, 2005.

[116]

V. J. Rayward-Smith, "Metaheuristics for clustering in KDD," in Proc. IEEE Congr. Evol. Comput., 2005, pp. 2380-2387.

[117]

K. S. N. Ripon, C.-H. Tsang, S. Kwong, and M.-K. Ip, "Multi-objective evolutionary clustering using variable-length real jumping genes genetic algorithm," in Proc. 18th Int. Conf. Pattern Recognit. (ICPR 2006), pp. 1200-1203.

Digital Library

[118]

E. Ruspini, "Numerical methods for fuzzy clustering," Inf. Sci., vol. 2, pp. 319-350, 1970.

Digital Library

[119]

I. Sarafis, "Data mining clustering of high dimensional databases with evolutionary algorithms," Ph.D. dissertation, Heriot-Watt Univ., Edinburgh, U.K., 2005.

[120]

L. Sarafis, P. W. Trinder, and A. M. S. Zalzala, "NOCEA: A rule-based evolutionary algorithm for efficient and effective clustering of massive high-dimensional databases," Appl. Soft Comput., vol. 7, no. 3, pp. 668- 710, Jun. 2007.

Digital Library

[121]

P. Scheunders, "A genetic C-means clustering algorithm applied to color image quantization," Pattern Recognit., vol. 30, pp. 859-866, 1997.

Digital Library

[122]

W. Sheng and X. Liu, "A hybrid algorithm for K-medoid clustering of large data sets," in Proc. IEEE Congr. Evol. Comput., 2004, pp. 77-82.

[123]

R. Srikanth, R. George, N. Warsi, D. Prabhu, F. E. Petry, and B. P. Buckles, "A variable-length genetic algorithm for clustering and classification," Pattern Recognit. Lett., vol. 16, pp. 789-800, 1995.

Digital Library

[124]

A. Strehl and J. Ghosh, "Cluster ensembles--A knowledge reuse framework for combining partitions," J. Mach. Learn. Res., vol. 3, pp. 583- 617, 2002.

Digital Library

[125]

H. Sun, S. Wang, and Q. Jiang, "FCM-based model selection algorithms for determining the number of clusters," Pattern Recognit. Lett., vol. 37, pp. 2027-2037, 2004.

Digital Library

[126]

A. Topchy, A. Jain, and W. Punch, "A mixture model for clustering ensembles," in Proc. SIAM Int. Conf. Data Mining, 2004, pp. 331- 338.

[127]

O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, D. Botstein, and R. B. Altman, "Missing value estimation methods for DNA microarray," Bioinformatics, vol. 17, pp. 520-525, 2001.

[128]

L. Y. Tseng and S. B. Yang, "A genetic approach to the automatic clustering problem," Pattern Recognit., vol. 34, pp. 415-424, 2001.

[129]

F. Valafar, "Pattern recognition techniques in microarray data analysis: A survey," Ann. New York Acad. Sci., vol. 980, pp. 41-64, 2002.

[130]

T. V. Le, "Evolutionary fuzzy clustering," in Proc. IEEE Congr. Evol. Comput., 1995, pp. 753-758.

[131]

X. L. Xie and G. Beni, "A validity measure for fuzzy clustering," IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 8, pp. 841-847, Aug. 1991.

Digital Library

[132]

R. Xu and D. Wunsch, II, "Survey of clustering algorithms," IEEE Trans. Neural Netw., vol. 16, no. 3, pp. 645-678, May 2005.

Digital Library

[133]

H.-S. Yoon, S.-Y. Ahn, S.-H. Lee, S.-B. Cho, and J. H. Kim, "Heterogeneous clustering ensemble method for combining different cluster results," in Proc. BioDM 2006. Lecture Notes in Computer Science, vol. 3916, pp. 82-92.

Digital Library

[134]

B. Yuan, G. J. Klir, and J. F. Swan-Stone, "Evolutionary fuzzy C-means clustering algorithm," in Proc. Int. Conf. Fuzzy Syst., 1995, pp. 2221- 2226.

Cited By

Yazdani DBranke JKhorshidi MOmidvar MLi XGandomi AYao XLi XHandl J(2024)Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation With Heterogeneous ChangesProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654188(50-58)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654188
Morimoto CPozo Ade Souto M(2024)An adaptive evolutionary multi-objective clustering based on the data properties of the base partitionsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.123102245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.123102
Brusa LPennoni FBartolucci F(2024)Maximum likelihood estimation for discrete latent variable models via evolutionary algorithmsStatistics and Computing10.1007/s11222-023-10358-534:2Online publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1007/s11222-023-10358-5
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Index Terms

A survey of evolutionary algorithms for clustering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews Volume 39, Issue 2

March 2009

118 pages

ISSN:1094-6977

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Copyright © 2009.

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IEEE Press

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Published: 01 March 2009

Revised: 17 April 2008

Received: 13 December 2007

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Yazdani DBranke JKhorshidi MOmidvar MLi XGandomi AYao XLi XHandl J(2024)Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation With Heterogeneous ChangesProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654188(50-58)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654188
Morimoto CPozo Ade Souto M(2024)An adaptive evolutionary multi-objective clustering based on the data properties of the base partitionsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.123102245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.123102
Brusa LPennoni FBartolucci F(2024)Maximum likelihood estimation for discrete latent variable models via evolutionary algorithmsStatistics and Computing10.1007/s11222-023-10358-534:2Online publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1007/s11222-023-10358-5
Moya AVeloso BGama JVentura S(2024)Improving hyper-parameter self-tuning for data streams by adapting an evolutionary approachData Mining and Knowledge Discovery10.1007/s10618-023-00997-738:3(1289-1315)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10618-023-00997-7
Martín JPontes BRiquelme JSilva SPaquete L(2023)Simultaneous Evolutionary Optimization of Features Subset and Clusters NumberProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3590603(307-310)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3590603
Baldassi C(2022)Recombinator-k-Means: An Evolutionary Algorithm That Exploits k-Means++ for RecombinationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.314413426:5(991-1003)Online publication date: 1-Oct-2022
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Zhu QTang XElahi A(2022)Automatic clustering based on dynamic parameters harmony search optimization algorithmPattern Analysis & Applications10.1007/s10044-022-01065-425:4(693-709)Online publication date: 1-Nov-2022
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Kaur AKumar Y(2022)A multi-objective vibrating particle system algorithm for data clusteringPattern Analysis & Applications10.1007/s10044-021-01052-125:1(209-239)Online publication date: 1-Feb-2022
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