NDLI: A New Clustering Method Based on References, Density and Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Hong-Tu Gou Jian-Suo Xu Li Wang
Copyright Year	2006
Description	Author affiliation: Sch. of Manage., Tianjin Univ. (Hong-Tu Gou)
Abstract	In this paper, a new clustering algorithm that is called RDVS (clustering using references and density by ViSOM) is presented to overcome the shortcomings of clustering methods based on density or neural network. The creativity of RDVS is capturing the shape and extent of a cluster by references and their densities, and then analyzes them by ViSOM. RDVS keeps the ability of density-based clustering method's good features and it can give a visual clustering results. Both theory analysis and experimental results confirm that RDVS can discover clusters with arbitrary shape and is insensitive to noise data, and its executing efficiency is much higher than ViSOM
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	1060
Ending Page	1065
File Size	238992
Page Count	6
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258560
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering methods Neural networks Data mining Clustering algorithms Shape Computer network management Technology management Machine learning Machine learning algorithms Computer networks data mining Clustering neural network density reference
Content Type	Text
Resource Type	Article

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