Center-based clustering of interval data / (Record no. 665)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03268nam a2200277 4500 |
| 001 - CONTROL NUMBER | |
| control field | UPMIN-00000014632 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | UPMIN |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230106115540.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230106b |||||||| |||| 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 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) | |
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) | LG993.5 2005 |
| Local cutter number (OCLC) ; Book number/undivided call number, CALL (RLIN) | A64 D45 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Denate, Ellen May B. |
| 9 (RLIN) | 1175 |
| 245 00 - TITLE STATEMENT | |
| Title | Center-based clustering of interval data / |
| Statement of responsibility, etc. | Ellen May B. Denate. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Date of publication, distribution, etc. | 2005 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 136 leaves |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2005 |
| 520 3# - SUMMARY, ETC. | |
| Summary, etc. | This paper presents clustering methods in handling interval data based on center-based clustering algorithms. Two clustering approaches were proposed, the Redefined K-Median algorithm and the Interval K-Mean algorithm. The Hausdorff distance is used in the Redefined K-Median algorithm and the Euclidean Distance Squared is utilized in the interval K-Means algorithm. The proposed algorithms were compared with existing Dynamic Clustering algorithms designed for interval data types (Standard Dynamic clustering using de Carvalho?s distance and Dynamical Clustering using Hausdorff distance) and Standard Clustering algorithms (Standard K-Median and Standard K-Means algorithms). The Corrected Rand (CR) index was utilized in comparing the different results of the algorithms. The proposed algorithms were tested using two sets of randomly generated artificial data following two sets of parameters. The CR index between the parameter-defined classes and the results of the different algorithms were computed to determine the recovery of the parameter-defined classes by the different algorithms used. The comparison between the parameter-defined classes and the final clustering results of the different algorithms showed that the Dynamic Clustering algorithms and the proposed algorithms have high recovery of the parameter-defined classes. However, the standard deviation for the Dynamic Clustering algorithms are higher than that of proposed algorithm implying that the results of the proposed algorithms are more stable. It was also seen in the CR index between the final clustering results of the different algorithms that the proposed algorithms are more similar results to the Dynamic Clustering algorithms than to the Standard Clustering algorithms. The proposed approaches have certain features that makes them likely choice of algorithm in clustering interval data depending on the size of the data set, the characteristics of the data set, and the number of clusters. The proposed approaches are also superior to the standard clustering algorithms when dealing with handling interval data since results show that the proposed algorithms can recover more of the predefined classes/realistic classification. The usual practice of computing the mean of the intervals and applying it to the K-Means or K-Median algorithm is not sufficient in getting the optimum clustering. The proposed approaches add to the options of clustering methods for handling interval data. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 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 | Center-based clustering. |
| 9 (RLIN) | 1177 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Interval data. |
| 9 (RLIN) | 1178 |
| 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) | |
| Source of classification or shelving scheme | Library of Congress Classification |
| Koha item type | Thesis |
| 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 | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Library of Congress Classification | Not For Loan | Preservation Copy | University Library | University Library | Archives and Records | 2008-04-15 | donation | UAR-T-gd1009 | LG993.5 2005 A64 D45 | 3UPML00031668 | 2022-09-21 | 2022-09-21 | Thesis | ||||
| Library of Congress Classification | Not For Loan | Room-Use Only | College of Science and Mathematics | University Library | Theses | 2007-04-08 | donation | CSM-T-gd1564 | LG993.5 2005 A64 D45 | 3UPML00011609 | 2022-09-21 | 2022-09-21 | Thesis |