k-medians clustering

Centroid

Determining the number of clusters in a data set

Cluster (spacecraft)

Complete-linkage clustering

Value (mathematics)

Mathematics

Correlation clustering

Algorithm

Image (mathematics)

Similarity (geometry)

Data mining

Statistics

World Wide Web

Artificial intelligence

Set (abstract data type)

Programming language

Single-linkage clustering

Index (typography)

Computer science

Data set

Cluster analysis

CURE data clustering algorithm

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.

Analysis of determining centroid clustering x-means algorithm with davies-bouldin index evaluation