A decentralized gossip based approach for data clustering in peer-to-peer networks

Azimi, 2017, A novel clustering algorithm based on data transformation approaches, Expert Syst. Appl., 76, 59, 10.1016/j.eswa.2017.01.024 A.R. Barakbah, Y. Kiyoki, A pillar algorithm for k-means optimization by distance maximization for initial centroid designation, in: The IEEE Symposium on Computational Intelligence and Data Mining, Nashville, 2009. P.S. Bradley, U.M. Fayyad, Refining initial points for k-means clustering, in: ICML ’98 Proceedings of the Fifteenth International Conference on Machine Learning, 1998, pp. 91–99. Chen, 2016, Distributed information-based clustering of heterogeneous sensor data, Signal Process., 126, 35, 10.1016/j.sigpro.2015.12.017 Cuzzocrea, 2013, Models and algorithms for high-performance distributed data mining, J. Parallel Distrib. Comput., 73, 281, 10.1016/j.jpdc.2012.11.002 S. Datta, C. Giannella, H. Kargupta, K–means clustering over a large, dynamic network, in: Proc. SIAM Int’l Conf. Data Mining, 2006, pp. 153–164. Datta, 2009, Approximate distributed k-means clustering over a peer-to-peer network, IEEE Trans. Knowl. Data Eng., 21, 1372, 10.1109/TKDE.2008.222 Datta, 2009, Approximate distributed k-means clustering over a peer-to-peer network, IEEE Trans. Knowl. Data Eng., 21, 1372, 10.1109/TKDE.2008.222 Di Fatta, 2013, Fault tolerant decentralised k-means clustering for asynchronous large-scale networks, J. Parallel Distrib. Comput., 73, 317, 10.1016/j.jpdc.2012.09.009 M. Eisenhardt, W. Muller, A. Henrich, Classifying documents by distributed P2P clustering, in: Proceedings of GI Jahrestagung (2), Vol. 35, 2003, pp. 286-291. M. Ester, H.P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: Proceedings of Knowledge Discovery in Database, KDD, 1996, pp. 226–231. Hubert, 1985, Comparing partitions, J. Classification, 2, 193, 10.1007/BF01908075 Li, 2011, A multi-dimensional trust evaluation model for large-scale P2P computing, J. Parallel Distrib. Comput., 71, 837, 10.1016/j.jpdc.2011.01.007 Lloyd, 1982, Least squares quantization in PCM, IEEE Trans. Inform. Theory, 28, 129, 10.1109/TIT.1982.1056489 MacQueen, 1967, Some methods for classification and analysis of multivariate observations, 281 Malinen, 2014, K-means*: Clustering by gradual data transformation, Pattern Recognit., 47, 3376, 10.1016/j.patcog.2014.03.034 Mashayekhi, 2015, GDCluster: a general decentralized clustering algorithm, IEEE Trans. Knowl. Data Eng., 27, 1892-1905, 10.1109/TKDE.2015.2391123 Mashayekhi, 2013, GoSCAN: Decentralized scalable data clustering, Computing, 95, 759, 10.1007/s00607-012-0264-2 A. Montresor, M. Jelasity, PeerSim: a scalable P2P simulator, in: Proc. of the 9th Int. Conference on Peer-to-Peer, P2P’09, Seattle, USA, Sept. 2009, pp. 99–100. Pena, 1999, An empirical comparison of the initialization methods for the k-means algorithm, Pattern Recognit. Lett., 20, 1027, 10.1016/S0167-8655(99)00069-0 Rougier, 2011, Dynamic self-organising map, Neurocomputing, 74, 1840, 10.1016/j.neucom.2010.06.034 Rousseeuw, 1987, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math., 20, 53, 10.1016/0377-0427(87)90125-7 The University of Eastern Finland, Color Research Laboratory, http://cs.uef.fi/sipu/datasets. Tzortzis, 2014, The minmax k-means clustering algorithm, Pattern Recognit., 47, 2505, 10.1016/j.patcog.2014.01.015 UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets.html . Voulgaris, 2005, CYCLON: Inexpensive membership management for unstructured P2P overlays, J. Netw. Syst. Manage., 13, 10.1007/s10922-005-4441-x Voulgaris, 2007, Proactive gossip-based management of semantic overlay networks, Concurr. Comput.: Pract. Exp., 19, 2299, 10.1002/cpe.1225 W. Xu, X. Liu, Y. Gong, Document clustering based on non-negative matrix factorization, in: Proc. of Int. Conf. on Research and Development in Information Retrieval, 2003, pp. 267–273. Zeng, 2012, Distributed data mining: a survey, Inf. Technol. Manage., 13, 403, 10.1007/s10799-012-0124-y