MARC details
| 000 -LEADER |
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| fixed length control field |
02338nam a22003013a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
UPMIN-00004810098 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
UPMIN |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20221020162411.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
221020b |||||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
DLC |
| Transcribing agency |
UPMin |
| Modifying agency |
upmin |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
eng |
| 090 #0 - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
LG993.5 2010 |
| Local cutter number (OCLC) ; Book number/undivided call number, CALL (RLIN) |
C6 M66 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Molina, Chieckerzon C. |
| 245 ## - TITLE STATEMENT |
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| Title |
Multi-elitist particle swarm optimization-tabu search (MEPSO-TS) applied in data clustering / |
| Statement of responsibility, etc. |
Chieckerzon C. Molina. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Date of publication, distribution, etc. |
2010 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
103 leaves. |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2010 |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Data clustering is an act of partitioning an unlabeled data set into groups of similar objects. Each group called a cluster consists of objects that are similar between themselves and dissimilar to object of others clusters. This project aims to find an alternative method of clustering continuous data set using hybrid method using two predefined methods. The involved algorithms in the study are Multi-Elitist Particle Swarm Optimization and Tabu Search. There are many cases that classical PSO and Tabu Search are combined and achieved an outstanding outcome. With this, the study improvised the hybrid method by using modified approach for each algorithm: Multi-Elitist for the classical PSO and searching modifications on the existing Tabu Search wherein the study use the idea of swapping points. After implementing and repetitive testing with 30 runs for each combination of parameter settings, the graph shows the comparison between hybrid algorithms and its counterpart as well as the hybrid MEPSO-TS against other existing hybrid method like PSO-TS. The results dictated the domination of the MEPSO-TS against other algorithms in terms of the solution quality. But, have failed to achieve optimal solution time in some cases of the Comparison. Thus, further analysis on how to deal with time optimization maintaining solution quality would fill in this study |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Data clustering |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Hybrid methods |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Multi-elitist particle swarm optimization (MEPSO) |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Tabu search (TS) |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Multi-elitist particle swarm optimization-tabu search (MEPSO-TS) |
| 658 ## - INDEX TERM--CURRICULUM OBJECTIVE |
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| Main curriculum objective |
Undergraduate Thesis |
| Curriculum code |
CMSC200, |
| Source of term or code |
BSCS |
| 905 ## - LOCAL DATA ELEMENT E, LDE (RLIN) |
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| a |
Fi |
| 905 ## - LOCAL DATA ELEMENT E, LDE (RLIN) |
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| a |
UP |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |
