Data clustering system on categorical data using modified K-modes clustering algorithms / (Record no. 2464)

MARC details
000 -LEADER
fixed length control field 01882nam a22002893a 4500
001 - CONTROL NUMBER
control field UPMIN-00004810092
003 - CONTROL NUMBER IDENTIFIER
control field UPMIN
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20221024173505.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 221024b |||||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency DLC
Transcribing agency UPMin
Modifying agency upmin
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
090 #0 - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) LG993.5 2009
Local cutter number (OCLC) ; Book number/undivided call number, CALL (RLIN) C6 P37
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Parreño, Cherry Lyn N.
245 ## - TITLE STATEMENT
Title Data clustering system on categorical data using modified K-modes clustering algorithms /
Statement of responsibility, etc. Cherry Lyn N. Parreño
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Date of publication, distribution, etc. 2009
300 ## - PHYSICAL DESCRIPTION
Extent 63 leaves.
502 ## - DISSERTATION NOTE
Dissertation note Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2009
520 3# - SUMMARY, ETC.
Summary, etc. This study focused on implementing a system which clusters categorical data with or without missing values using two modified K-modes algorithms namely, the available case analysis and the adaptive imputation. Files with .txt extensions are the only ones accepted the system. The file or data set must only contain the data points or the instances of a given data. The original K-modes algorithm was first implemented and then modified according to the available case analysis algorithm and adaptive imputation algorithm. The available case analysis has a modified distance measure and computation for the few cluster centers to cater data sets with missing values. The adaptive imputation also has a different dissimilarity measure and computation for the new cluster center; it applied imputation on the data during the first iteration. The results were displayed in tabular form along with its final cluster centers. File uploading was made available in the system for the users ease. It can upload at most four data sets at a time
650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Categorical data
650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Clustering
650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element K-modes algorithm
650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Missing values
658 ## - INDEX TERM--CURRICULUM OBJECTIVE
Main curriculum objective Undergraduate Thesis
Curriculum code CMSC200,
Source of term or code BSCS
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
a Fi
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
a UP
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Thesis
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Status Collection Home library Current library Shelving location Date acquired Source of acquisition Accession Number Total Checkouts Full call number Barcode Date last seen Koha item type
    Library of Congress Classification   Not For Loan Preservation Copy University Library University Library Archives and Records 2010-07-06 donation UAR-T-gd1594   LG993.5 2010 C6 P37 3UPML00033247 2022-10-05 Thesis
    Library of Congress Classification   Not For Loan   College of Science and Mathematics University Library General Reference 2010-05-13 donation CSM-T-gd2287   LG993.5 2009 C6 P37 3UPML00012601 2022-10-05 Thesis
 
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