Analysis of Various Clustering Techniques with Centroid Initialized K-Means Clustering
Shi Yong; Zhang Ge; “Research on an improved algorithm for cluster analysis”, International Conference on Consumer Electronics, Communications and Networks (CECNet), Pp. 598 – 601, 2011.
Gkalelis, N.; Mezaris, V.; Kompatsiaris, I.; “Mixture Subclass Discriminant Analysis”, IEEE Signal Processing Letters, Vol. 18, No. 5, Pp. 319 – 322, 2011.
Weijiang Jiang; Jun Ye; “Decision-making method based on an improved similarity measure between vague sets”, IEEE 10th International Conference on Computer-Aided Industrial Design & Conceptual Design (CAID & CD), Pp. 2086 – 2090, 2009.
Gil-Garcia, R.; Badia-Contelles, J.M.; Pons-Porrata, A.; “A General Framework for Agglomerative Hierarchical Clustering Algorithms”, 18th International Conference on Pattern Recognition (ICPR), Vol. 2, Pp. 569 – 572, 2006.
de Souza, R.M.C.; de Carvalho, F.A.T.; “A Clustering Method for Mixed Feature-Type Symbolic Data using Adaptive Squared Euclidean Distances”, 7th International Conference on Hybrid Intelligent Systems (HIS), Pp. 168 – 173, 2007.
Tasoulis, D.K.; Plagianakos, V.P.; Vrahatis, M.N.; “Clustering in evolutionary algorithms to efficiently compute simultaneously local and global minima”, The 2005 IEEE Congress on Evolutionary Computation, Vol. 2, Pp. 1847 – 1854, 2005.
Chen, B.; Tai, P.C.; Harrison, R.; Yi Pan; “Novel hybrid hierarchical-K-means clustering method (H-K-means) for microarray analysis”, IEEE Computational Systems Bioinformatics Conference, Pp. 105 – 108, 2005.
Wei-Chuan Liu; Jiun-Long Huang; Ming-Syan Chen; “KACU: k-means with hardware centroid-updating”, Emerging Information Technology Conference, DOI: 10.1109/EITC.2005.1544347, 2005.
Kehar Singh, Dimple Malik and Naveen Sharma, “Evolving limitations in K-means algorithm in data mining and their removal”, IJCEM International Journal of Computational Engineering & Management, Vol. 12, Pp. 105-109, 2011.
Yinghua Zhou; Hong Yu; Xuemei Cai; “A Novel k-Means Algorithm for Clustering and Outlier Detection”, Second International Conference on Future Information Technology and Management Engineering (FITME '09), Pp. 476 – 480, 2009.
P. S. Bradley, and U. M. Fayyad, “Refining Initial Points for K-Means Clustering,” ACM, Proceedings of the 15th International Conference on Machine Learning, pp. 91-99, 1998.
F. Yang, T. Sun, and C. Zhang, “An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization,” An International Journal on Expert Systems with Applications, vol. 36, no. 6, pp. 9847-9852, 2009.
Aristidis Likas, Nikos Vlassis, and Jakob J. Verbeek, “The global k-means clustering algorithm,” The Journal of Pattern Recognition society, Elsevier, vol. 36, no. 2, pp. 451-461, 2003.
Zhexue Huang, “Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values,” Journal on Data Mining and Knowledge Discovery, Springer, vol. 2, no. 3, pp. 283-304, 1998.
Xue Sun; Kunlun Li; Rui Zhao; Xikun Hu; “Global Optimization for Semi-supervised K-means”, Asia-Pacific Conference on Information Processing (APCIP), Vol. 2, Pp. 410 – 413, 2009.
Junjie Wu; Hui Xiong; Jian Chen; Wenjun Zhou; “A Generalization of Proximity Functions for K-Means”, Seventh IEEE International Conference on Data Mining (ICDM), Pp. 361 – 370, 2007.
Kuncheva, L.I.; Vetrov, D.P.; “Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 11, Pp. 1798 – 1808, 2006.
Khan, D.M.; Mohamudally, N.; “A multiagent system (MAS) for the generation of initial centroids for k-means clustering data mining algorithm based on actual sample datapoints”, 2nd International Conference on Software Engineering and Data Mining (SEDM), Pp. 495 – 500, 2010.
Yan Zhu; Jian Yu; Caiyan Jia; “Initializing K-means Clustering Using Affinity Propagation”, Ninth International Conference on Hybrid Intelligent Systems (HIS '09), Vol. 1, Pp. 338 – 343, 2009.
Jieming Wu; Wenhu Yu; “Optimization and Improvement Based on K-Means Cluster Algorithm”, Second International Symposium on Knowledge Acquisition and Modeling (KAM '09), Vol. 3, Pp. 335 – 339, 2009.
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