Abstract
The task of clustering is important and the most difficult part of the overall problem of Data Mining because it is based on the self-learning paradigm, i.e. implies the absence of pre-tagged training sample. In real conditions, this task is complicated by the fact that in having data arrays some of the observations can be corrupted by anomalous outliers and some - contain missing data, that is, the “object-property” table has “empty” cells. In addition, data can be arrive in online mode on processing, especially for tasks related with Data Stream Mining and Big Data. In the paper the problem of fuzzy adaptive online clustering of data distorted by outliers that are sequentially fed to the processing when the original sample volume and the number of distorted observations are apriori unknown is considered. The probabilistic and possibilistic adaptive online clustering algorithms for such data, that are based on the similarity measure of a special type that weaken or overwhelming outliers are proposed. The computational experiment confirms the effectiveness of approach under consideration.
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References
Rutkowski L (2008) Computational intelligence: methods and techniques. Springer, Heidelberg
Sepkovski JJ (1974) Quantified coefficients of association and measurement of similarity. J Int Assoc Math 6(2):135–152
Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum, New York
Yang YK, Shieh HL, Lee CN (2004) Constructing a fuzzy clustering model based on its data distribution. In: International conference on computational intelligence for modeling, control and automation (CIMCA 2004), Gold Coast, Australia
Nikolova M (2004) A variational approach to remove outliers and impulse noise. J Math Imaging Vis 20(1–2):99–120
Davé RN, Sen S (1997) Noise clustering algorithm revisited. In: NAFIPS 1997, 21–24 September 1997, pp 199–204
Gabrys B, Bargiela A (2000) General fuzzy min-max neural network for clustering and classification. IEEE Trans Neural Netw 11(3):769–783
Hathaway RJ, Bezdek JC, Hu Y (2000) Generalized fuzzy c-means clustering strategiesusing Lp norm distances. IEEE Trans Fuzzy Syst 8(5):576–582
Davé RN, Sen S (2002) Robust fuzzy clustering of relational data. IEEE Trans Fuzzy Syst 10(6):713–727
Comaniciu D, Meer P (2002) Mean shift: a robust approach toward features pace analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619
Dave RN, Krishnapuram R (1997) Robust cluatering methods: a unified view. IEEE Trans Fuzzy Syst 5(2):270–293
Zhang J, Leung Y (2003) Robust clustering by pruning outliers. IEEE Trans Syst Man Cybern 33(6):983–999
Ren LX, Irwin G (2003) Robust fuzzy Gustafson-Kessel clustering for nonlinear system identification. Int J Syst Sci 34(14–15):787–803
Leski J (2003) Towards a robust fuzzy clustering. Fuzzy Sets Syst 137(2):215–233
Honda LK, Sugiura N, Ichihashi H (2003) Robust local principal component analyzer with fuzzy clustering. In: Proceedings of the international joint conference on neural networks, vol l, pp 732–737
Yang MS, Wu KL (2004) A similarity-based robust clustering method. IEEE Trans Pattern Anal Mach Intell 26(4):434–448
Keller L, Krishnapuram R, Pal NR (2005) Fuzzy models and algorithms for pattern recognition and image processing. Springer, New York
Shafronenko A, Dolotov A, Bodyanskiy Y, Setlak G (2018) Fuzzy clustering of distorted observations based on optimal expansion using partial distances. In: 2018 IEEE second international conference on data stream mining and processing (DSMP), pp 327–330
Pal NR, Pal K, Keller JM et al (2005) A possibilistic fuzzy c-means clustering algorithm. IEEE Trans Fuzzy Syst 13(4):517–530
Kokshenev I, Bodyanskiy Y, Gorshkov Y, Kolodyazhniy V (2006) Outlier resistant recursive fuzzy clustering algorithm. In: Reusch B (ed) Computational intelligence: theory and application. Advances in soft computing, vol 38. Springer, Heidelberg, pp 647–652
Cai W, Chen S, Zhang D (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn 40(3):825–838
Banerjee (2009) Robust fuzzy clustering as a multi-objective optimization procedure. In: Proceeding soft the 28th annual meeting of the North American fuzzy information processing society (NAFIPS 2009), June 2009
Bezdek JC (2013) Pattern recognition with fuzzy objective function algorithms. Springer, New York
Ji Z, Liu J, Cao G, Sun Q, Chen Q (2014) Robust spatially constrained fuzzy c-means algorithm for brain MR image segmentation. Pattern Recogn 47:2454–2466
Krinidisand S, Chatzis V (2010) A robust fuzzy local information c-means clustering algorithm. IEEE Trans Image Process 19(5):1328–1337
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Bodyanskiy, Y., Shafronenko, A., Mashtalir, S. (2020). Online Robust Fuzzy Clustering of Data with Omissions Using Similarity Measure of Special Type. In: Lytvynenko, V., Babichev, S., Wójcik, W., Vynokurova, O., Vyshemyrskaya, S., Radetskaya, S. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2019. Advances in Intelligent Systems and Computing, vol 1020. Springer, Cham. https://doi.org/10.1007/978-3-030-26474-1_44
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