Clustering of uninhabitable houses using the optimized apriori algorithm

Abstract

Clustering is one of the roles in data mining which is very popularly used for data problems in solving everyday problems. Various algorithms and methods can support clustering such as Apriori. The Apriori algorithm is an algorithm that applies unsupervised learning in completing association and clustering tasks so that the Apriori algorithm is able to complete clustering analysis in Uninhabitable Houses and gain new knowledge about associations. Where the results show that the combination of 2 itemsets with a tendency value for Gas Stove fuel of 3 kg and the installed power meter for the attribute item criteria results in a minimum support value of 77% and a minimum confidence value of 87%. This proves that a priori is capable of clustering Uninhabitable Houses to help government work programs.