<![CDATA[Clustering process aims to group all objects based on their proximity. K-Means is oneof clustering algorithms which is categorized as center-based clustering algorithm. In this algorithm, the value of k is an input parameter that has no prior information. If the k is too small then there is one or more clusters in the result that has a big SSE. To reduce the SSE, this kind of cluster has to be split in to two or more sub-clusters. This paper introduces a new method to split a cluster into two clusters (bisecting) based on minimum forest graph which is called Bisecting Minimum Forest Graph (BMFG). To measure the quality, we used two indicators, i.e. information gain and compactness-separation criterion. The result shows that this method gives a better performance compared with Forgy method. Based on the results, BMFG method produced a maximum purity of each cluster and a maximum consistency index for all the runs. On the other hand, Forgy method achieved the maximum consistency only on well distributed data. For CSC index, BMFG and Forgy methods yielded the similar results. However, for the dataset with relatively not well distributed (noisy), BMFG provided a better index of CSC.]]>
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