Evaluation of Estimating Number of Clusters Methods in Case of Agglomerative Hierarchical Clustering

The main goal of data mining process is to extract information and discover knowledge from huge databases, where the clustering is one of the most important functionalities which can be done in this area. There are many of clustering algorithms and methods, but determining or estimating the number of clusters which should be extracted from a dataset is one of the most important issues most of these methods encounter it. This research focuses on the problem of estimating number of clusters in the case of agglomerative hierarchical clustering. We present an evaluation of three of the most common methods used in estimating number of clusters.