NDLI: An improved k-means clustering algorithm

Content Provider	IEEE Xplore Digital Library
Author	Wang Yintong Li Wanlong Gao Rujia
Copyright Year	2012
Description	Author affiliation: Computer Science and Engineering, Changchun University of Technology, China (Wang Yintong; Li Wanlong; Gao Rujia)
Abstract	K-means algorithm used in the initial cluster centers are randomly generated, then clustering results are unstable and susceptible to the noise data-objects. In this paper presents a density-based algorithm to determine the initial cluster centers, eliminate the clustering results depend on the initial cluster centers. While, optimized the methods of cluster centers re-calculation and the distance from data-object to the cluster center, reduce noise impact on the clustering results, which meets the clustering of asymmetry density cluster. Experiments on UCI datasets show that the improved algorithm can eliminate the clustering results depend on the initial cluster centers, obtain more compact cluster. Therefore, the improved K-means clustering algorithm is effective.
Starting Page	1
Ending Page	3
File Size	177477
Page Count	3
File Format	PDF
ISBN	9781467344975
ISSN	21544824
e-ISBN	9781889334479
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-24
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	TSI Press
Subject Keyword	Clustering algorithms Classification algorithms Algorithm design and analysis Iris Machine learning algorithms Noise Partitioning algorithms initial cluster center clustering K-means algorithm
Content Type	Text
Resource Type	Article

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