Abstract
Data clustering algorithmic schemes receive much careful research insight due to the prominent role that clustering plays during data analysis. Proper data clustering reveals data structure and makes possible further data processing and analysis. In the application area, k-means clustering algorithms are most often exploited in almost all important branches of data processing and data exploration. During last decades, a great deal of new algorithmic techniques have been invented and implemented that extend basic k-means clustering methods. In this context, fuzzy and rough k-means clustering presents robust modifications of basic k-means clustering that are aimed at better apprehension of data structure that advantageously incorporate notions from fuzzy and rough set theories. In the paper, an extension of rough k-means clustering into rough entropy domain has been introduced. Experimental results suggest that proposed algorithm outperforms standard k-means clustering methods applied in the area of image segmentation.
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References
Asharaf, S., Murty, M.N.: A Rough Fuzzy Approach to Web Usage Categorization. Fuzzy Sets and Systems 148, 119–129 (2004)
Lingras, P., West, C.: Interval Set Clustering of Web Users with Rough k-Means. Journal of Intelligent Information Systems 23(1), 5–16 (2004)
Maji, P., Pal, S.K.: RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets. Fundamenta Informaticae 80(4), 477–498 (2007)
Malyszko, D., Stepaniuk, J.: Granular Multilevel Rough Entropy Thresholding in 2D Domain. In: IIS 2008, 16th International Conference Intelligent Information Systems, Zakopane, Poland, June 16-18, pp. 151–160 (2008)
Malyszko, D., Stepaniuk, J.: Standard and Fuzzy Rough Entropy Clustering Algorithms in Image Segmentation. In: Chan, C.-C., Grzymala-Busse, J.W., Ziarko, W.P. (eds.) RSCTC 2008. LNCS (LNAI), vol. 5306, pp. 409–418. Springer, Heidelberg (2008)
Pal, S.K., Shankar, B.U., Mitra, P.: Granular computing, rough entropy and object extraction. Pattern Recognition Letters 26(16), 2509–2517 (2005)
Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177(1), 3–27 (2007); Rough sets: Some extensions. Information Sciences 177(1), 28–40 (2007); Rough sets and Boolean reasoning. Information Sciences 177(1), 41–73 (2007)
Pedrycz, W., Skowron, A., Kreinovich, V. (eds.): Handbook of Granular Computing. John Wiley & Sons, New York (2008)
Stepaniuk, J.: Rough–Granular Computing in Knowledge Discovery and Data Mining. Springer, Heidelberg (2008)
Zhou, T., Zhang, Y.N., Lu, H.L.: Rough k-means Cluster with Adaptive Parameters. In: Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, August 19-22, pp. 3063–3068 (2007)
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Małyszko, D., Stepaniuk, J. (2009). Rough Entropy Based k-Means Clustering. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_49
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DOI: https://doi.org/10.1007/978-3-642-10646-0_49
Publisher Name: Springer, Berlin, Heidelberg
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