Rough set based clustering for finding relevant document / (Record no. 4630)

MARC details
000 -LEADER
fixed length control field 01929nam a2200265 a 4500
001 - CONTROL NUMBER
control field vtls000077203
003 - CONTROL NUMBER IDENTIFIER
control field KUKTEM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251114204604.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 140403t2013 my a f m 000 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number THE0001971(Local)
039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE]
Level of rules in bibliographic description 201905131548
Level of effort used to assign nonsubject heading access points yusri
-- 201404031355
-- Fida
040 ## - CATALOGING SOURCE
Original cataloging agency UMP
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) QA76.9.F5 N43 2013 rs Bc.
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Ng Choon Ching
245 10 - TITLE STATEMENT
Title Rough set based clustering for finding relevant document /
Statement of responsibility, etc. Ng Choon Ching
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Kuantan, Pahang :
Name of publisher, distributor, etc. UMP,
Date of publication, distribution, etc. 2013
300 ## - PHYSICAL DESCRIPTION
Extent x, 52 p. :
Other physical details ill. (some col.) ;
Dimensions 30 cm. +
Accompanying material 1 CD-ROM
502 ## - DISSERTATION NOTE
Dissertation note Project paper (Bachelor of Computer Science (Software Engineering) -- Universiti Malaysia Pahang - 2013
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Bibliography : p. 51-52
520 3# - SUMMARY, ETC.
Summary, etc. Searching 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 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element File organization (Computer science)
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Document clustering
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Application software
General subdivision Development
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Date acquired Total checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
  Not lost   Not for loan UMPLIB GAMBANG UMPLIB GAMBANG 04/09/2019   CD 7602 | QA76.9.F5 N43 2013 rs Bc. 0000078847 04/09/2019 1 04/09/2019 Final Year Report
  Not lost   Not for loan UMPLIB PEKAN UMPLIB PEKAN 04/09/2019   QA76.9.F5 N43 2013 rs Bc. 0000078846 04/09/2019 1 04/09/2019 Final Year Report

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