| dc.description.abstract | At present, an analysis of the data or information is important in determining or divides
into several clusters. Clustering method is one way how to analyze the static data that is
used in various fields, including machine learning, data mining, pattern recognition,
image analysis, information retrieval, and bioinformatics. Clustering is a common
technique and unsurpervised learning. In addition, other terms of similar meaning to the
clustering are automatic classification, numerical taxonomy, botryology and typological
analysis. In the clustering there are various methods that can be used, one of which is
the K-mean clustering algorithm. This algorithm is simple and easy to understand and is a popular algorithm used. In the K-mean clustering, data sets will be divided into
clusters that have been determined. Where K is the number of clusters needed to analyze the data, the cluster formed by the closest distance to the centroid of the set. Therefore, applications such as simulation tool to run the K-mean clustering algorithm are very high. Thus, this simulation tool used to evaluate the efficiency and effectiveness of Kmean clustering algorithm. | en_US |
