Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/43249
Title: Initialization of cluster refinement algorithms: A review and comparative study
Authors: He, J. 
Lan, M.
Tan, C.-L. 
Sung, S.-Y. 
Low, H.-B.
Issue Date: 2004
Source: He, J.,Lan, M.,Tan, C.-L.,Sung, S.-Y.,Low, H.-B. (2004). Initialization of cluster refinement algorithms: A review and comparative study. IEEE International Conference on Neural Networks - Conference Proceedings 1 : 297-302. ScholarBank@NUS Repository.
Abstract: Various iterative refinement clustering methods are dependent on the initial state of the model and are capable of obtaining one of their local optima only. Since the task of identifying the global optimization is NP-hard, the study of the initialization method towards a sub-optimization is of great value. This paper reviews the various cluster initialization methods in the literature by categorizing them into three major families, namely random sampling methods, distance optimization methods, and density estimation methods. In addition, using a set of quantitative measures, we assess their performance on a number of synthetic and real-life data sets. Our controlled benchmark identifies two distance optimization methods, namely SCS and KKZ, as complements of the K-Means learning characteristics towards a better cluster separation in the output solution.
Source Title: IEEE International Conference on Neural Networks - Conference Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/43249
ISSN: 10987576
Appears in Collections:Staff Publications

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