Title :
A novel self-organizing map algorithm for text mining
Author :
Yang, Hsin-Chang ; Lee, Chung-Hong
Author_Institution :
Dept. of Inf. Manage., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan
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.
Keywords :
data mining; learning (artificial intelligence); self-organising feature maps; text analysis; data mining; document clusters; learning algorithm; machine learning; pattern recognition; self organizing map algorithm; text mining; Pattern recognition; learning algorithm; self-organizing map; text mining; theme identification;
Conference_Titel :
System Science and Engineering (ICSSE), 2010 International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-6472-2
DOI :
10.1109/ICSSE.2010.5551734