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

Autor:	Saut Dohot Siregar, Fadhillah Azmi, Insidini Fawwaz, Amir Mahmud Husein, N P Dharshinni
Rok vydání:	2019
Předmět:	History ComputingMethodologies_PATTERNRECOGNITION Computer science k-means clustering InformationSystems_DATABASEMANAGEMENT A priori and a posteriori Patient data Data mining computer.software_genre computer Computer Science Applications Education
Zdroj:	Journal of Physics: Conference Series. 1230:012020
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/1230/1/012020
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::660dfec33b2fb91a5c6c913fb5c5171a https://doi.org/10.1088/1742-6596/1230/1/012020 Zobrazit plný text záznamu