Open AccessBayesian Hierachical Clustering for Bidikmisi Environment: Results of successful and unsuccessful scholarship cluster
Author(s) -
Wahyuni Suryaningtyas,
Nur Iriawan,
Heri Kuswanto,
Ismaini Zain
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/469/1/012092
Subject(s) - cluster analysis , bayesian probability , homogeneity (statistics) , cluster (spacecraft) , multivariate statistics , computer science , mathematics , statistics , data mining , artificial intelligence , programming language
This research focuses on conducting Bidikmisi data clusters. Group analysis or cluster analysis is one of the multivariate techniques used with the aim of classifying an observation that has certain characteristics. This analysis groups observations into a group so that in one group there are similarities or homogeneity with each other. In contrast, intergroup is expected to have differences or heterogeneity. The clustering process uses the Bayesian Hierachical Clustering (BHC) approach, aiming to present a complete taxonomy of grouping evaluation measurements that will be used for empirical studies.