MARC details
| 000 -LEADER |
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| fixed length control field |
02105nam a2200277 4500 |
| 001 - CONTROL NUMBER |
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| control field |
UPMIN-00000014631 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
UPMIN |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20230209114954.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230209b |||||||| |||| 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 ## - 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 2006 |
| Local cutter number (OCLC) ; Book number/undivided call number, CALL (RLIN) |
A64 R47 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Responso, Crisemhar Robledo. |
| 9 (RLIN) |
2257 |
| 245 00 - TITLE STATEMENT |
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| Title |
A particle swarm optimization-simulated annealing (PSO-SA) hybrid for data clustering / |
| Statement of responsibility, etc. |
Crisemhar Robledo Responso. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Date of publication, distribution, etc. |
2006 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
84 leaves |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2006 |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Data clustering is a problem that deals with classification of objects within the data set into clusters such that items in the same cluster have a high degree of similarity. Known heuristic algorithms are applied to solve the problem. In this study, Particle Swarm Optimization (PSO) hybrid with Simulated Annealing (SA) was used to cluster data on Iris data set. PSO is relatively new family of algorithm, which is a population ?based stochastic optimization technique while SA is an algorithm, which is a population-based stochastic optimization technique while SA is an algorithm that concerns with finding global extremum of the function and works on a single solution. Different sets of parameter values were tested on the algorithm to determine which setting best suits the data. Results showed that smaller parameter values for SA and PSO parameters except of inertia weight performed significantly faster while larger parameter values of all parameter except inertia gave better solution quality. The result also showed that number of hits or assignment of data to a cluster is somewhat bad. However, PSO-SA algorithm is still a promising alternative to cluster data on Iris data set if further improvements can be done. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Data clustering. |
| 9 (RLIN) |
1176 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Particle Swarm Optimization(PSO). |
| 9 (RLIN) |
2258 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Simulated annealing. |
| 9 (RLIN) |
1370 |
| 658 ## - INDEX TERM--CURRICULUM OBJECTIVE |
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| Main curriculum objective |
Undergraduate Thesis |
| Curriculum code |
AMAT200, |
| Source of term or code |
BSAM |
| 905 ## - LOCAL DATA ELEMENT E, LDE (RLIN) |
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| a |
Fi |
| 905 ## - LOCAL DATA ELEMENT E, LDE (RLIN) |
|---|
| 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 |
