Abstract
The data clustering with automatic program such as k-means has been a popular technique widely used in many general applications. Two interesting sub-activity of clustering process are studied in this paper, selection the number of clusters and analysis the result of data clustering. This research aims at studying the clustering validation to find appropriate number of clusters for k-means method. The characteristics of experimental data have 3 shapes and each shape have 4 datasets (100 items), which diffusion is achieved by applying a Gaussian distributed (normal distribution). This research used two techniques for clustering validation: Silhouette and Sum of Squared Errors (SSE). The research shows comparative results on data clustering configuration k from 2 to 10. The results of both Silhouette and SSE are consistent in the sense that Silhouette and SSE present appropriate number of clusters at the same k-value (Silhouette value: maximum average, SSE-value: knee point).
Highlights
We study the clustering validity techniques to quantify the appropriate number of clusters for k-means algorithm
Which found that the density of the k values of k = 2 and k = 4 show the density and separation is optimal
It was found that the average of all silhouette values when k=4 the highest shown in table 1
Summary
The clustering is similar to the data classification in terms of data input, the clustering is learning without target class. The clustering algorithm forms groups based on object similarities[1]. The clustering was applied to many fields such as bioinformatics, genetics, image processing, speech recognition, market research, document classification, and weather classification[2]. The clustering was applied to document data analysis that was one of big data learning[3,4,5,6,7]. There are various algorithms for the data clustering
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