Web News Documents Clustering in Indonesian Language Using Singular Value Decomposition-Principal Component Analysis and Ant Algorithms
Ant-based document clustering is a cluster method of measuring text documents similarity based on the shortest path between nodes (trial phase) and determines the optimal clusters of sequence do- cument similarity (dividing phase). The processing time of trial phase Ant algorithms to make docu- ment vectors is very long because of high dimensional Document-Term Matrix (DTM). In this paper, we proposed a document clustering method for optimizing dimension reduction using Singular Value Decomposition-Principal Component Analysis (SVDPCA) and Ant algorithms. SVDPCA reduces size of the DTM dimensions by converting freq-term of conventional DTM to score-pc of Document-PC Matrix (DPCM). Ant algorithms creates documents clustering using the vector space model based on the dimension reduction result of DPCM. The experimental results on 506 news documents in Indo- nesian language demonstrated that the proposed method worked well to optimize dimension reduction up to 99.7%. We could speed up execution time efficiently of the trial phase and maintain the best F- measure achieved from experiments was 0.88 (88%).
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