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Concept induction via fuzzy C-means clustering in a high dimensional semantic space.

Song, Dawei; Cao, Guihong; Bruza, Peter D.; Lau, Raymond Y.K.


Dawei Song

Guihong Cao

Peter D. Bruza

Raymond Y.K. Lau


Jos´┐Ż Valente de Oliveira

Witold Pedrycz


Lexical semantic space models have recently been investigated to automatically derive the meaning (semantics) of information based on natural language usage. In a semantic space, a term can be considered as a concept represented geometrically as a vector, the components of which correspond to terms in a vocabulary. A primary way to perform reasoning in a semantic space is to categorize concepts in the space into a number of regions (i.e., groups). Such a process is referred to as concept induction, which can be realized by clustering objects in the space. The resulting groups can potentially form a basis for knowledge discovery and ontology construction. Conventional clustering algorithms, e.g., the K-Means method, normally produce crisp clusters, i.e., an object could be assigned to only one cluster. It is not always the case in reality. For example, a word Reagan may belong to both the cluster about administration of US government, and another one about the Iran-contra scandal. Therefore, a membership function is applied, which determines the degree to which an object belongs to different clusters. This chapter introduces a cognitively motivated semantic space model, namely Hyperspace Analogue to Language (HAL), and shows how a fuzzy C-Means clustering algorithm is used to concept categorization in the high dimensional semantic space. The experimental results indicate that applying fuzzy C-Means clustering over the HAL semantic space is promising in constructing semantically related groups of terms.


SONG, D., CAO, G., BRUZA, P.D. and LAU, R.Y.K. 2007. Concept induction via fuzzy C-means clustering in a high dimensional semantic space. In Valente de Oliveira, J. and Pedrycz, W. (eds.) Advances in fuzzy clustering and its applications. Chichester: Wiley [online], chapter 19, pages 393-403. Available from:

Online Publication Date Apr 30, 2007
Publication Date Apr 30, 2007
Deposit Date Mar 13, 2009
Publicly Available Date Mar 13, 2009
Publisher Wiley
Pages 393-403
Book Title Advances in fuzzy clustering and its applications
Chapter Number Chapter 19
ISBN 9780470027608
Keywords Clustering algorithms; Fuzzy C-Means clustering algorithm; Hyperspace; Analogue
Public URL
Publisher URL


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