ANR: An algorithm to recommend initial cluster centers for k-means algorithm

Clustering is one of the widely used knowledge discovery techniques to detect structure of datasets and can be extremely useful to the analyst. In center based clustering algorithms such as k-means, choosing initial cluster centers is really important as it has an important impact on the clustering result. It is desirable to select initial centers which are well separated. In this paper, we have proposed an algorithm to find initial cluster centers based on choosing two attributes that can describe the data space better and using the number of neighbors in a specific radius in data space. The proposed Attribute and Neighborhood Radius based (ANR) initial cluster center computing algorithm is applied to several well-known datasets .experimental results shows that it prevents form choosing noise data points as cluster center and tries to choose data points from dense areas in data space.