Open AccessWEB NEWS DOCUMENTS CLUSTERING IN INDONESIAN LANGUAGE USING SINGULAR VALUE DECOMPOSITION-PRINCIPAL COMPONENT ANALYSIS (SVDPCA) AND ANT ALGORITHMS
Author(s) -
Arif Fadllullah,
Dasrit Debora Kamudi,
Muhamad Nasir,
Agus Zainal Arifin,
Diana Purwitasari
Publication year - 2016
Publication title -
jurnal ilmu komputer dan informasi (journal of computer science and information)/jurnal ilmu komputer dan informasi
Language(s) - English
Resource type - Journals
eISSN - 2502-9274
pISSN - 2088-7051
DOI - 10.21609/jiki.v9i1.362
Subject(s) - document clustering , singular value decomposition , cluster analysis , computer science , principal component analysis , dimensionality reduction , dimension (graph theory) , data mining , similarity (geometry) , algorithm , pattern recognition (psychology) , artificial intelligence , mathematics , pure mathematics , image (mathematics)
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 document similarity (dividing phase). The processing time of trial phase Ant algorithms to make document 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 Indonesian 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%).