High performance in minimizing of term-document matrix representation for document clustering

B., Baharudin and L., Muflikhah (2009) High performance in minimizing of term-document matrix representation for document clustering. In: 2009 Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2009, 25 July 2009 through 26 July 2009, Kuala Lumpur.

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Document clustering usually involves high dimensional term space, which makes it difficult for organizing data into a small number of meaningful clusters. Clustering based on similar terms without considering the content or meaning is often unsatisfactory as it ignores the relationship between important terms that do not co-occur literally. In this paper, we propose to integrate the Latent Semantic Indexing (LSI) concept to our document clustering. This involves the use of Singular Value Decomposition (SVD) which creates a new abstract and uses a way of finding pattern document collection in matrix representation, so that it can identify between the terms and documents which are similar. By using various numbers of patterns (rank) of SVD, the proposed method is applied to cluster documents using the Fuzzy C-Means algorithm. The results of the experiment show that the performance of document clustering to be better when appliedto the LSI method. © 2009 IEEE.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Cluster documents; Document Clustering; Document matrices; Fuzzy C-means algorithms; High-dimensional; Latent semantic indexing; Matrix representation; Pattern documents; Cluster analysis; Copying; Fuzzy clustering; Industrial applications; Information retrieval; Intelligent systems; LSI circuits; Security of data; Singular value decomposition
Subjects:Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments / MOR / COE:Departments > Computer Information Sciences
ID Code:185
Deposited By: Dr Baharum Baharudin
Deposited On:24 Feb 2010 15:01
Last Modified:19 Jan 2017 08:25

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