Open AccessAnalysis of determining centroid clustering x-means algorithm with davies-bouldin index evaluation
Author(s) -
M Mughnyanti,
Syahril Efendi,
Muhammad Zarlis
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/725/1/012128
Subject(s) - centroid , cluster analysis , similarity (geometry) , single linkage clustering , cluster (spacecraft) , data mining , complete linkage clustering , set (abstract data type) , k medians clustering , index (typography) , mathematics , value (mathematics) , data set , algorithm , computer science , determining the number of clusters in a data set , correlation clustering , cure data clustering algorithm , statistics , artificial intelligence , image (mathematics) , world wide web , programming language
Clustering is a process to group data into several clusters or groups so the data in one cluster has a maximum level of similarity and data between clusters has a minimum similarity. X-means clustering is used to solving one of the main weaknesses of K-means clustering need for prior knowledge about the number of clusters (K). In this method, the actual value of K is estimated in a way that is not monitored and only based on the data set itself. The results of the study using the X-Means algorithm with the Davies-Bouldin Index evaluation to determine the number of Centroid clusters is done by modifying the X-Means method to do some centroid determination to get 11 iterations. The result is produces cluster members that have a good level of similarity with other data. In determining the number of centroids, use the Davies-Bouldin Index method where testing with 2 clusters has a minimum value with a DBI value close to 0.