Analysis of Accuracy K-Means and Apriori Algorithms for Patient Data Clusters

Abstract

The stacking data is usefull to get a new information. Data mining is a methode to determine the important pattern in Frequent Itemset Mining (FIM). Apriori is part of association rule that is used to determine the assosiative relationship in items combination. But apriori has a high computational time weakness because frequent itemset process searching must scan the database repeatedly for each itemset combination. This study aims to see the effect of the k-means clustering algorithm on the apriori algorithm by combining these two algorithms. The test results show that the combination of k-means and apriori algorithms produces more information detaily and faster time computating than the apriori algorithm with a total computing time of 21.93 minutes and a combination of k-means and apriori algorithms 17.41 minutes.