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02051nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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ft6078 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20251127120800.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
251127b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA76.9.A43 C39 2016 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Cayabyab, Renee Anne S. and Contreras, Justine Mariel L. |
| 245 ## - TITLE STATEMENT |
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| Title |
An enhancement dbscan algorithm applied to faculty performance evaluation |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2016 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Other physical details |
Undergraduate Thesis: (BSCS major in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2016. |
| 336 ## - CONTENT TYPE |
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text |
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text |
| Content type code |
text |
| 337 ## - MEDIA TYPE |
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unmediated |
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unmediated |
| Media type code |
unmediated |
| 338 ## - CARRIER TYPE |
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volume |
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volume |
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volume |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
ABSTRACT: DBSCAN is a fundamental density cluster formation. Its advantage is that it can discover clusters with arbitrary shapes and size. The algorithm typically regards clusters as dense regions of objects in the data space that are separated by regions of low-density objects. The algorithm has two input parameters, radius Eps and MinPts. It only needs to find out all the maximal density connected spaces to cluster the data points in an attribute space. And these density-connected spaces are the clusters. Every object not contained in any cluster is considered noise and can be ignored. Though DBSCAN has a lot of advantages compared to other clustering algorithms, it also has its own drawbacks. First, the algorithm is not entirely deterministic wherein it cannot determine the correct cluster of a border point. Second, DBSCAN is sensitive to the setting of parameters resulting to unfitting clusters. Third, possible merging of two supposedly separate clusters may occur in the existing algorithm, failing to comply with the goal of cluster analysis. The researchers were able to enhance the existing system by solving the problems stated and have met the objectives presented. The enhanced algorithm satisfied the standards of the possible user through the conducted survey. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| Classification |
Filipiniana |
| 655 ## - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Source of classification or shelving scheme |
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| Item type |
Archival materials |