Study on Influence of Effectiveness Factor in HCM Algorithm in 2-D Space

The classic hard c-means (HCM) is totally based on Euclid Distance, and it cannot cope with situations of different cluster sizes. Method of attaching an effectiveness factor to each distance item (HCMef) is proposed, transforming the criterion based on distance into the more general criterion based on angle.Two-cluster data sets in 2-D space, normally distributed, with the similar density, the cluster boundaries from vague through clear, and the contrast of cluster population 1000∶1000, 1000∶5000 and 1000∶10000 respectively, are experimented. The results show that HCMef can find the pre-established cluster centers faster and more precisely. The advantages of HCMef are obvious under various situations. The feasibility of HCMef in 2-D space with 2 clusters are verified and the further study is worth performing.