Determining the number of clusters in a dataset is fundamental issue data clustering. Many methods have been proposed to solve problem selecting clusters, considering it be with regard model selection. This paper proposes an efficient algorithm that automatically selects suitable based on probability distribution framework. The includes following two components. First, generalization Kohonen's ...

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this i...

The self-organizing map is discussed as an unsupervised clustering method. Its ability to form clusters indicates similar features in a data set. Based on this property, it is demonstrated that a self-organizing map is capable of identifying features within software code by grouping procedures with similar properties together. This allows us to identify potential objects, abstract data types or...

In this article, we report our implementation and comparison of two text clustering techniques. One is based on Ward’s clustering and the other on Kohonen’s Self-organizing Maps. We have evaluated how closely clusters produced by a computer resemble those created by human experts. We have also measured the time that it takes for an expert to ‘‘clean up’’ the automatically produced clusters. The...