Enhancing an evolving tree-based text document visualization model with fuzzy c-Means clustering
Contribuinte(s) |
[Unknown] |
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Data(s) |
01/01/2013
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Resumo |
An improved evolving model, i.e., Evolving Tree (ETree) with Fuzzy c-Means (FCM), is proposed for undertaking text document visualization problems in this study. ETree forms a hierarchical tree structure in which nodes (i.e., trunks) are allowed to grow and split into child nodes (i.e., leaves), and each node represents a cluster of documents. However, ETree adopts a relatively simple approach to split its nodes. Thus, FCM is adopted as an alternative to perform node splitting in ETree. An experimental study using articles from a flagship conference of Universiti Malaysia Sarawak (UNIMAS), i.e., Engineering Conference (ENCON), is conducted. The experimental results are analyzed and discussed, and the outcome shows that the proposed ETree-FCM model is effective for undertaking text document clustering and visualization problems. |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
http://dro.deakin.edu.au/eserv/DU:30057147/chang-enhancinganevolving-2013.pdf http://dro.deakin.edu.au/eserv/DU:30057147/evid-conffuzzieee-rvwgnl-2013.pdf |
Direitos |
2013, IEEE |
Palavras-Chave | #evolving tree #text document clustering #visualization #online learning #fuzzy c-means |
Tipo |
Conference Paper |