Abstract
Affinity Propagation (AP) is a recently proposed clustering technique, widely used in literature, which finds the cluster center by exchanging real messages between pairs of data. These messages are calculated on the basis of similarity matrix. Accordingly, the similarity matrix is considered an essential procedure of the AP. Negative Euclidean distance is used as a similarity measure, in order to construct the similarity matrix of the AP. However, most data points lie in the non-Euclidean space which becomes difficult for the Euclidean distance to acquire the real data structure. The performance of AP might be degraded if this drawback occurs. A clustering method is proposed here called Intuitionistic Fuzzy Affinity Propagation (IFAP) that uses an intuitionistic fuzzy similarity measure to construct the similarity matrix among data points. Subsequently, the similarity matrix is fed into the AP procedure, and the cluster center will emerge after a couple of iterations. The numerical experiment is demonstrated. Results show that the IFAP outperforms the other clustering method.
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References
Yang J et al (2010) Affinity propagation feature clustering with application to vehicle detection and tracking in road traffic surveillance. In: 2010 Seventh IEEE international conference on advanced video and signal based surveillance (AVSS). IEEE
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. Oakland, CA, USA
Kaufman L, Rousseeuw P (1987) Clustering by means of medoids. North-Holland
Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814):972–976
Yang C et al (2010) A fuzzy-statistics-based affinity propagation technique for clustering in multispectral images. IEEE Trans Geosci Remote Sens 48(6):2647–2659
Jiang Y, Liao Y, Yu G (2016) Affinity propagation clustering using path based similarity. Algorithms 9(3):46
Wang X, Wang Y, Wang L (2004) Improving fuzzy c-means clustering based on feature-weight learning. Pattern Recogn Lett 25(10):1123–1132
Keller JM, Gray MR, Givens JA (1985) A fuzzy k-nearest neighbor algorithm. IEEE Trans Syst Man Cybern 4:580–585
Burrough PA et al (2001) Fuzzy k-means classification of topo-climatic data as an aid to forest mapping in the Greater Yellowstone Area, USA. Landscape Ecol 16(6):523–546
Tran D, Wagner H (1999) Fuzzy expectation-maximisation algorithm for speech and speaker recognition. In: Fuzzy information processing society, 1999. NAFIPS. 18th International conference of the North American. IEEE
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353
Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203
Tian W et al (2014) Research on clustering based meteorological data mining methods. Adv Sci Technol Lett 79:106–112
Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96
Dueck D, Frey BJ (2007) Non-metric affinity propagation for unsupervised image categorization. In: IEEE 11th international conference on computer vision, 2007. ICCV 2007. IEEE
Kang Y, Choi S (2009) Common neighborhood sub-graph density as a similarity measure for community detection. In: International conference on neural information processing, Springer
Guan R et al (2011) Text clustering with seeds affinity propagation. IEEE Trans Knowl Data Eng 23(4):627–637
Geweniger T et al (2009) Fuzzy variant of affinity propagation in comparison to median fuzzy c-means. In: International workshop on self-organizing maps. Springer
Li P et al (2018) Dynamic equivalent modeling of two-staged photovoltaic power station clusters based on dynamic affinity propagation clustering algorithm. Int J Electr Power Energy Syst 95:463–475
Liu J, Zhao X-D, Xu Z-H (2017) Identification of rock discontinuity sets based on a modified affinity propagation algorithm. Int J Rock Mech Min Sci 94:32–42
Shrivastava SK, Rana J, Jain R (2013) Fast affinity propagation clustering based on machine learning
Xu Z (2013) Intuitionistic fuzzy aggregation and clustering, vol 279. Springer
Kacprzyk J (1997) Multistage fuzzy control: a prescriptive approach. Wiley
Szmidt E, Kacprzyk J (2000) Distances between intuitionistic fuzzy sets. Fuzzy Sets Syst 114(3):505–518
Grzegorzewski P (2004) Distances between intuitionistic fuzzy sets and/or interval-valued fuzzy sets based on the Hausdorff metric. Fuzzy Sets Syst 148(2):319–328
Yager RR (1979) On the measure of fuzziness and negation part I: membership in the unit interval
Zhang X et al (2010) K-AP: generating specified K clusters by efficient affinity propagation. In: 2010 IEEE 10th international conference on data mining (ICDM). IEEE
Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier
Dheeru D, Taniskidou EK (2017) UCI machine learning repository. Retrieved from http://archive.ics.uci.edu/ml
Fowlkes EB, Mallows CL (1983) A method for comparing two hierarchical clusterings. J Am Stat Assoc 78(383):553–569
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Akash, O.M., Ahmad, S.S.S., Azmi, M.S., Alkouri, A.U.M. (2019). Affinity Propagation Based on Intuitionistic Fuzzy Similarity Measure. In: Piuri, V., Balas, V., Borah, S., Syed Ahmad, S. (eds) Intelligent and Interactive Computing. Lecture Notes in Networks and Systems, vol 67. Springer, Singapore. https://doi.org/10.1007/978-981-13-6031-2_30
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DOI: https://doi.org/10.1007/978-981-13-6031-2_30
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