01695nam a2200217 a 4500001001400000003000700014005001700021008004100038020002200079040000800101100001900109245007900128260003400207300005500241502010800296504002800404520094200432650004101374650002401415650003801439vtls000077203KUKTEM20251114204604.0140403t2013    my a   f m    000 0 eng d  aTHE0001971(Local)  aUMP1 aNg Choon Ching10aRough set based clustering for finding relevant document /cNg Choon Ching  aKuantan, Pahang :bUMP,c2013  ax, 52 p. :bill. (some col.) ;c30 cm. +e1 CD-ROM  aProject paper (Bachelor of Computer Science (Software Engineering) -- Universiti Malaysia Pahang - 2013  aBibliography : p. 51-523 aSearching for relevant documents based on the keywords of particular selected articles  are proposed in this thesis. This method is proposed to help user get relevant document based on the articles they selected.  The common searching engine will return up to thousand articles where some articles are not really relevant to the searching too.  In this paper, rough set-based data mining technique is employed to enhance the result of searching relevant documents. The rough set-based clustering technique, namely MinMin Roughness (MMR) is applied to cluster documents from Wikipedia into groups according to keywords of selected articles in the effort for finding relevant documents. This research is done using dataset of articles from online Wikipedia website. The proposed keywords methods for finding relevant documents will save time during searching progress. This research is expected to be useful for finding relevant documents. 0aFile organization (Computer science) 0aDocument clustering 0aApplication softwarexDevelopment