NDLI: K-means clustering with multiresolution peak detection

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2004 IEEE Electro/Information Technology Conference

K-means clustering with multiresolution peak detection

Content Provider	IEEE Xplore Digital Library
Author	Guanshan Yu Leen-Kiat Soh Bond, A.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Nebraska-Lincoln Univ., Lincoln, NE (Guanshan Yu; Leen-Kiat Soh)
Abstract	Clustering is a practical data mining approach of pattern detection. Because of the sensitivity of initial conditions, k-means clustering often suffers from low clustering performance. We present a procedure to refine initial conditions of k-means clustering by analyzing density distributions of a data set before estimating the number of clusters k necessary for the data set, as well as the positions of the initial centroids of the clusters. We demonstrate that this approach indeed improves the accuracy and performance of k-means clustering measured by average intra to inter-clustering error ratio. This method is applied to the virtual ecology project to design a virtual blue jay system
Starting Page	6
Ending Page	6
File Size	172031
Page Count	1
File Format	PDF
ISBN	0780392329
DOI	10.1109/EIT.2005.1626978
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-05-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Environmental factors Testing Data mining Computer science Data engineering Bonding Biological system modeling Frequency Biology Machine learning
Content Type	Text
Resource Type	Article

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