A New Index for Clustering Evaluation Based on Density Estimation
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A new index for internal evaluation of clustering is introduced. The index is defined as a mixture of two sub-indices. The first sub-index I_a is called the Ambiguous Index; the second sub-index I_s is called the Similarity Index. Calculation of the two sub-indices is based on density estimation to each cluster of a partition of the data. An experiment is conducted to test the performance of the new index, and compared with three popular internal clustering evaluation indices – Calinski-Harabasz index, Silhouette coefficient, and Davies-Bouldin index, on a set of 145 datasets. The result shows the new index improves the three popular indices by 59 correspondingly.
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