NDLI: A novel self-organizing map algorithm for text mining

Content Provider	IEEE Xplore Digital Library
Author	Hsin-Chang Yang Chung-Hong Lee
Copyright Year	2010
Description	Author affiliation: Department of Information Management, National University of Kaohsiung, Taiwan (Hsin-Chang Yang) \|\| Department of Electrical Engineering, National Kaohsiung University of Applied Sciences, Taiwan (Chung-Hong Lee)
Abstract	Self-organizing map (SOM) learning algorithm has been widely applied in solving various tasks in pattern recognition, machine learning, and data mining, etc. Recently, it has been used to cluster documents and produced reasonable results. Traditional SOM algorithm learns from data using a fixed map. Approaches have been proposed to allow adaptable map structure. In this work, we propose a novel SOM learning algorithm that can expand the map laterally and hierarchically. The adaption of the map structure is based on topics identified from the underlying document clusters. This approach is different from traditional approaches which are typically driven by the data density of clusters. Preliminary experiment result suggested that the proposed algorithm outperforms other similar approaches.
Starting Page	417
Ending Page	420
File Size	182511
Page Count	4
File Format	PDF
ISBN	9781424464722
e-ISBN	9781424464746
DOI	10.1109/ICSSE.2010.5551734
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-01
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Text mining Self-organizing map Pattern recognition Learning algorithm Theme identification
Content Type	Text
Resource Type	Article

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