MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02129nam a22002893a 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
UPMIN-00000518117 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
UPMIN |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20230209165948.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
230209b |||||||| |||| 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 #0 - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
LG993.5 2007 |
| Local cutter number (OCLC) ; Book number/undivided call number, CALL (RLIN) |
A64 S27 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Sarmiento, Jon Marx P. |
| 9 (RLIN) |
561 |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
D-neighborhood imputation method for ordinal data sets with missing values / |
| Statement of responsibility, etc. |
Jon Marx P. Sarmiento |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Date of publication, distribution, etc. |
2007 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
111 leaves. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2007 |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Imputation is applied in filling up missing values in surveys which are ordinal in form. Among the imputation techniques are Mean, Mode, Hot-deck and KNN imputations which have their own drawbacks. To address this issue, the proponent introduced a new imputation method called D-neighborhood imputation. It uses the concept of neighborhood and cut off value to ensure high similarity with the reference and the maximum penalty rule in solving for the distance of unknown values. D-neighborhood was evaluated and compared with the existing techniques. The experiment was done using the Dermatology and Breast Cancer data sets. Incomplete data sets were generated under MCAR with 1%, 5%, 10%, 20%, and 30% level of missing values and conditioned MCAR with 0.25, 0.5, 0.75 and 1 probability in no, 2, and 3 combinations. According to the results, it performed best under MCAR condition in both data sets and resulted the best clustering quality when applied to Breast Cancer data set under MAR condition. Using Dermatology data set, D-neighborhood and KNN have competing results while using Breast Cancer data set, D-neighborhood performed best. In general, D-neighborhood imputation outperformed the rest of the algorithms when tested in both data sets. |
| 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 |
K-means algorithm. |
| 9 (RLIN) |
2351 |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Imputation techniques. |
| 9 (RLIN) |
2352 |
| 650 17 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Ordinal data sets. |
| 9 (RLIN) |
2353 |
| 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 |
