Clustering is a method for grouping objects with similar patterns and finding meaningful clusters in a data set. There exist a large number of clustering algorithms in the literature, and the results of clustering even in a particular algorithm vary according to its input parameters such as the number of clusters, field weights, similarity measures, the number of passes, etc. Thus, it is important to effectively evaluate the clustering results a priori, so that the generated clusters are more close to the real partition. In this paper, an improved clustering validity assessment index is proposed based on a new density function for intercluster similarity and a new scatter function for intra-cluster similarity. Experimental results show the effectiveness of the proposed index on the data sets under consideration regardless of the choice of a clustering algorithm.