NDLI: An Experimental Comparison of Three Kinds of Clustering Algorithms

Content Provider	IEEE Xplore Digital Library
Author	Xuezhi Zheng Zhihua Cai Qu Li
Copyright Year	2005
Description	Author affiliation: Fac. of Comput. Sci., China Univ. of Geosciences, Wuhan (Xuezhi Zheng; Zhihua Cai; Qu Li)
Abstract	Clustering is one of the most important and well studied fields of data mining. Many clustering methods have been proposed in the last few decades with different backgrounds. An open problem in clustering field is the lack of a benchmark for contrasting all these algorithms as well as other available ones. In this paper, we use the well-known log marginal likelihood (LML) score and classification accuracy as two criteria of comparison. Experiment results on k-means, expectation maximization (EM) and farthest-first in the t-test show that EM outperforms other two algorithms on most of the benchmark data sets with respect to both criteria. Considering the performance and comprehensibility of EM, it is an ideal model that could be used in many real world applications
Starting Page	767
Ending Page	771
File Size	878647
Page Count	5
File Format	PDF
ISBN	0780394224
DOI	10.1109/ICNNB.2005.1614738
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Clustering methods Geology Software algorithms Clustering algorithms Educational institutions Iterative algorithms Distance measurement Electronic mail Data mining
Content Type	Text
Resource Type	Article

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