A Heuristic Automatic Clustering Method Based on Hierarchical Clustering
We propose a clustering method which produces valid results while automatically determining an optimal number of clusters. The proposed method achieves these results with minimal user input, of which none pertains to a number of clusters. Our method’s effectiveness in clustering, including its ability to produce valid results on data sets presenting nested or interlocking shapes, is demonstrated and compared with cluster validity analysis to other methods to which a known optimal number of clusters is provided, and to other automatic clustering methods. Depending on the particularities of the data set used, our method has produced results which are roughly equivalent or better than those of the compared methods.
KeywordsData-mining Automatic clustering Unsupervised learning
We gratefully acknowledge the support from NBIF’s (RAI 2012-047) New Brunswick Innovation Funding granted to Dr. Nabil Belacel.
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