MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02509nam a22003133a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
UPMIN-00003241232 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
UPMIN |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20230206173538.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230206b |||||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
|---|
| Transcribing agency |
UPMin |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| 090 #0 - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
LG993.5 2008 |
| Local cutter number (OCLC) ; Book number/undivided call number, CALL (RLIN) |
A64 O55 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Onggo, Raphael John Rule. |
| 9 (RLIN) |
2140 |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Nearest neighbor-based imputation in treating data sets with missing values and their effects in the clustering accuracy / |
| Statement of responsibility, etc. |
Raphael John Rule Onggo. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Date of publication, distribution, etc. |
2008 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
73 leaves. |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2008 |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
The K-Nearest Neighbor (KNN) imputation method, along with the more commonly used imputation methods mean and median imputations, were used in treating incomplete data sets. In order to obtain a clear comparison, three complete data sets were used with two types of missingness: missing completely at random (MCAR) and missing at random (MAR). missing values were generated from these complete data sets at rates 1%, 5%, 10%, and 20%. The treated incomplete data sets were then clustered using then k-mean clustering algorithm. The incomplete data sets were also clustered using the modified k-means clustering algorithms to the imputed data sets obtained from the three imputation methods were compared to each other and to that of the results obtained after applying the modified k-means clustering algorithm with adaptive imputation to the incomplete data sets. Results revealed that the k-nearest neighbor, mean, and medium imputation methods and the modified k-means clustering algorithm attained high cluster recovery even at 20% missing values. Furthermore, clustering results obtained from the k-nearest neighbor imputed data sets showed to have the most accurate clustering results as compared to the clustering results obtained from the mean imputed data sets and the median imputed data sets, and also the clustering results obtained after applying the modified k-means clustering algorithm with adaptive imputation to the incomplete data sets in MAR and MCAR types of missing values. |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Imputation methods. |
| 9 (RLIN) |
2141 |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
KNN (K-Nearest Neighbor) |
| 9 (RLIN) |
2142 |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
K-means clustering.. |
| 9 (RLIN) |
2093 |
| 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 |
MCAR (Missing completely at random) |
| 9 (RLIN) |
2103 |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Data sets. |
| 9 (RLIN) |
1992 |
| 658 ## - INDEX TERM--CURRICULUM OBJECTIVE |
|---|
| Main curriculum objective |
Undergraduate Thesis |
| Curriculum code |
AMAT200, |
| Source of term or code |
BSAM |
| 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 |
