Data spread-based entropy clustering method using adaptive learning

Clustering analysis is to identify inherent structures and discover useful information from large amount of data. However, the decision makers may suffer insufficient understanding the nature of the data and do not know how to set the optimal parameters for the clustering method. To overcome the drawback above, this paper proposes a new entropy clustering method using adaptive learning. The proposed method considers the data spreading to determine the adaptive threshold within parameters optimized by adaptive learning. Four datasets in UCI database are used as the experimental data to compare the accuracy of the proposed method with the listing clustering methods. The experimental results indicate that the proposed method is superior to the listing methods.