  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 six other internal clustering
evaluation indices -- Calinski-Harabasz index, Silhouette coefficient,
Davies-Bouldin index, CDbw, DBCV, and VIASCKDE, on a set of 145 datasets. The
result shows the new index significantly improves other internal clustering
evaluation indices.
