| 000 | 01929nam a2200265 a 4500 | ||
|---|---|---|---|
| 001 | vtls000077203 | ||
| 003 | KUKTEM | ||
| 005 | 20251114204604.0 | ||
| 008 | 140403t2013 my a f m 000 0 eng d | ||
| 020 | _aTHE0001971(Local) | ||
| 039 | 9 |
_a201905131548 _byusri _y201404031355 _zFida |
|
| 040 | _aUMP | ||
| 090 | _aQA76.9.F5 N43 2013 rs Bc. | ||
| 100 | 1 | _aNg Choon Ching | |
| 245 | 1 | 0 |
_aRough set based clustering for finding relevant document / _cNg Choon Ching |
| 260 |
_aKuantan, Pahang : _bUMP, _c2013 |
||
| 300 |
_ax, 52 p. : _bill. (some col.) ; _c30 cm. + _e1 CD-ROM |
||
| 502 | _aProject paper (Bachelor of Computer Science (Software Engineering) -- Universiti Malaysia Pahang - 2013 | ||
| 504 | _aBibliography : p. 51-52 | ||
| 520 | 3 | _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. | |
| 650 | 0 | _aFile organization (Computer science) | |
| 650 | 0 | _aDocument clustering | |
| 650 | 0 |
_aApplication software _xDevelopment |
|
| 999 |
_aVIRTUA40 _c4630 _d4636 |
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| 999 | _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*6502*9992 | ||