Open AccessDiscriminant AnalysisFor Cluster Validation In A Case Study of District Grouping In Jember Regency Based On Poverty
Author(s) -
Fikriaur Istiqomah,
Made Tirta,
Dian Anggareni
Publication year - 2019
Publication title -
jurnal ilmu dasar
Language(s) - English
Resource type - Journals
ISSN - 2442-5613
DOI - 10.19184/jid.v20i2.9862
Subject(s) - computer science , linkage (software) , data mining , linear discriminant analysis , cluster (spacecraft) , multivariate statistics , analytic hierarchy process , artificial intelligence , statistics , machine learning , mathematics , operations research , programming language , biochemistry , chemistry , gene
Cluster validation is a procedure to evaluate the results of cluster analysis quantitively and objectively on a data. The validation process is very important to get the results of a good and appropriate grouping. In the validation process, the author uses internal validation, stability, and discriminant analysis test. This study aims to obtain validation results from the hierarchy and kmeans method. This data grouping uses “iris” simulation data, which results from the grouping method used can be applied to the original data to see which vaidation method is used for all data and produce an optimal grouping. The result of the study show that in the “iris” data, a single linkage link is an appropriate grouping method because the result of the grouping are optimal for all validations and classification of group members whose groups are significant. In District poverty data in Jember Regency with a single linkage link optimal grouping was obtained and complete linkage links were also used as a method that resulted in optimal groupig for all validation. Cluster validation discriminant analysis test is appropriate for various types of data in general annd shows that single linkage methods are better than other methods for grouping and validation methods for “iris” data and District data in Jember Regency based on variabels of poverty status. Keywords: Cluster Analysis, Diskriminant Analysis, Multivariate Analysis, Validation Cluster.