NDLI: Dynamic self-organizing maps with controlled growth for knowledge discovery

Content Provider	IEEE Xplore Digital Library
Author	Alahakoon, D. Halgamuge, S.K. Srinivasan, B.
Copyright Year	1990
Abstract	The growing self-organizing map (GSOM) algorithm is presented in detail and the effect of a spread factor, which can be used to measure and control the spread of the GSOM, is investigated. The spread factor is independent of the dimensionality of the data and as such can be used as a controlling measure for generating maps with different dimensionality, which can then be compared and analyzed with better accuracy. The spread factor is also presented as a method of achieving hierarchical clustering of a data set with the GSOM. Such hierarchical clustering allows the data analyst to identify significant and interesting clusters at a higher level of the hierarchy, and continue with finer clustering of the interesting clusters only. Therefore, only a small map is created in the beginning with a low spread factor, which can be generated for even a very large data set. Further analysis is conducted on selected sections of the data and of smaller volume. Therefore, this method facilitates the analysis of even very large data sets.
Sponsorship	IEEE Computational Intelligence Society
Starting Page	601
Ending Page	614
Page Count	14
File Size	198822
File Format	PDF
ISSN	10459227
Volume Number	11
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-05-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Self organizing feature maps Data mining Unsupervised learning Data analysis Clustering algorithms Neural networks Humans Pattern analysis Computer science Software engineering
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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