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One of the applications of center-based clustering algorithms such as K-Meansis partitioning data points into K clusters. In some examples, the featurespace relates to the underlying problem we are trying to solve, and sometimeswe can obtain a suitable feature space. Nevertheless, while K-Means is one ofthe most efficient offline clustering algorithms, it is not equipped toestimate the number of clusters, which is useful in some practical cases. Otherpractical methods which do are simply too complex, as they require at least onerun of K-Means for each possible K. In order to address this issue, we proposea K-Means initialization similar to K-Means++, which would be able to estimateK based on the feature space while finding suitable initial centroids forK-Means in a deterministic manner. Then we compare the proposed method,DISCERN, with a few of the most practical K estimation methods, while alsocomparing clustering results of K-Means when initialized randomly, usingK-Means++ and using DISCERN. The results show improvement in both theestimation and final clustering performance.

DISCERN: Diversity-based Selection of Centroids for k-Estimation and Rapid Non-stochastic Clustering