2007 IEEE International Conference on Granular Computing (GRC 2007) A New Improved K-Means Algorithm with Penalized Term San Jose, California November 02-November 04 ISBN: 0-7695-3032-X
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/GrC.2007.39
K-means Algorithm is a popular method in cluster anal- ysis. After reviewing different K-means algorithms, we pro- pose the new penalized K-means algorithm. Originally in- spired by the Maximum Likelihood(ML) method, a prior probability distribution assumed by classic K-means algo- rithm about the clustering data set was discovered, and then the new objective function for the penalized K-means algo- rithm was introduced. By minimizing this function with ge- netic algorithm, results show that this method is better than K-means algorithm in some perspectives.
Citation:
Zejin Ding, Jian Yu, Yanqing Zhang, "A New Improved K-Means Algorithm with Penalized Term," grc, pp.313, 2007 IEEE International Conference on Granular Computing (GRC 2007), 2007 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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