MARC details
| 000 -LEADER |
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| fixed length control field |
02962nam a22002893a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
UPMIN-00005135667 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
UPMIN |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20221205145426.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
221205b |||||||| |||| 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 A64 |
| Local cutter number (OCLC) ; Book number/undivided call number, CALL (RLIN) |
C66 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Comendador, Christine Y. |
| 9 (RLIN) |
363 |
| 245 ## - TITLE STATEMENT |
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| Title |
Monothetic clustering algorithms based on measures of association / |
| Statement of responsibility, etc. |
Christine Y. Comendador |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Date of publication, distribution, etc. |
2010 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
111 leaves |
| 500 ## - GENERAL NOTE |
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| General note |
Thesis, Undergraduate (BS in Applied Mathematics - Operations Research) -- U.P. Mindanao |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Cluster analysis is one common technique that is used in multivariate analysis. Its objective is to maximize the dissimilarity of objects within the sane cluster and maximize the dissimilarity of object among different clusters. Monothetic analysis is a class of methods used for clustering binary data. Monothetic analysis algorithms produce a hierarchy of clusters in which each step a group is split into two clusters based on the values of one of the binary variables. However, existing monothetic algorithms use the simplest association measure. This study was done to test whether Yule?s q, Yule's y, Phi coefficient and percent difference could be used as a splitting criterion. The algorithm was modified in such a way that variables having identical values for all observations were ignored to avoid undefined values for the association between variables and by replacing the standard criterion with the selected association measures. The modified algorithms were tested with twenty simulated binary data resulted in identifying the same number of cluster. Nevertheless, the algorithms varied in the selection of splitting variable and in effect, results to different number of iterations performed. Yule?s y ? based MONA performs the fastest algorithm among the modified algorithms and Phi coefficient ? based MONA performs the slowest algorithm in terms of the number of iterations. However, percentage difference ?based MONA yields the fastest algorithm compared to all other algorithms in terms of processing time. Aside from the simulated data sets, an application to the habitat of 16 species to 129 communities was also used in this study. As a result, it confirms the fact that Percent difference ?based MONA performs the fastest algorithm compared to all algorithms in terms of processing time. Also, it confirms the fact that Phi coefficient ?based MONA performs slowest algorithm. Despite the fact that Percent difference ?based MONA is the fastest algorithm on both application to data sets, further study are still needed to verify the performance of these modified algorithms |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Association measures |
| 9 (RLIN) |
364 |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Binary data |
| 9 (RLIN) |
365 |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Clustering |
| 9 (RLIN) |
366 |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Monothetic analysis |
| 9 (RLIN) |
367 |
| 658 ## - INDEX TERM--CURRICULUM OBJECTIVE |
|---|
| Main curriculum objective |
Undergraduate Thesis |
| Curriculum code |
AMAT200 |
| 905 ## - LOCAL DATA ELEMENT E, LDE (RLIN) |
|---|
| 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 |
