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|Title: ||Concept induction via fuzzy C-means clustering in a high dimensional semantic space.|
|Authors: ||Song, Dawei|
Bruza, Peter D.
Lau, Raymond Y. K.
|Keywords: ||Clustering algorithms|
Fuzzy C-Means clustering algorithm
|Issue Date: ||2007|
|Citation: ||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: J. VALENTE DE OLIVEIRA and W.
PEDRYCZ, eds. Advances in fuzzy clustering and its applications.
Chichester: Wiley. Pp. 393-403.|
|Abstract: ||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.|
|Appears in Collections:||Book chapters (Computing)|
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