NDLI: A Novel Clustering Algorithm Based on Hierarchical and K-means Clustering

Content Provider	IEEE Xplore Digital Library
Author	Li Wenchao Zhou Yong Xia Shixiong
Copyright Year	2007
Description	Author affiliation: China Univ. of Min. & Technol., Xuzhou (Li Wenchao; Zhou Yong; Xia Shixiong)
Abstract	Although the priority and randomicity to initiate clustering centers of K-means have been solved by traditional hierarchical k-means clustering algorithm, the algorithm is difficult to be applied widespread popularly owing to its high computational complexity. So a novel clustering algorithm based on hierarchical and K-means clustering, which has good computational complexity, is proposed in this paper. Firstly, the concept of silhouette coefficient is introduced and the optimal clustering number Kopt included in data set of unknown class information is decided. Then the distribution of data set is gotten through hierarchical clustering and clustering center is decided. Finally, the clustering is completed through K-means clustering. The efficiencies of the algorithm is validated through the test of IRIS testing data set.
Starting Page	605
Ending Page	609
File Size	615551
Page Count	5
File Format	PDF
ISBN	9787811240559
DOI	10.1109/CHICC.2006.4347538
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science K-means Iris Clustering algorithms Silhouette coefficient Entropy Electronic mail Clustering Hierarchical clustering Computational complexity Testing
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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