NDLI: Two density-based k-means initialization algorithms for non-metric data clustering

Content Provider	Springer Nature Link
Author	Bianchi, Filippo Maria Livi, Lorenzo Rizzi, Antonello
Copyright Year	2015
Abstract	In this paper, we propose a density-based clusters’ representatives selection algorithm that identifies the most central patterns from the dense regions in the dataset. The method, which has been implemented using two different strategies, is applicable to input spaces with no trivial geometry. Our approach exploits a probability density function built through the Parzen estimator, which relies on a (not necessarily metric) dissimilarity measure. Being a representatives extractor a general-purpose algorithm, our method is obviously applicable in different contexts. However, to test the proposed procedure, we specifically consider the problem of initializing the k-means algorithm. We face problems defined on standard real-valued vectors, labeled graphs, and finally sequences of real-valued vectors and sequences of characters. The obtained results demonstrate the effectiveness of the proposed representative selection method with respect to other state-of-the-art solutions.
Starting Page	745
Ending Page	763
Page Count	19
File Format	PDF
ISSN	14337541
Journal	Pattern Analysis & Applications
Volume Number	19
Issue Number	3
e-ISSN	1433755X
Language	English
Publisher	Springer London
Publisher Date	2015-01-15
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Clustering Prototype selection k-means initialization Dissimilarity measures Non-metric domains Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition

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