In this paper, a cluster validity index called CDV index is presented. The CDV index is capable of providing a quality measurement for the goodness of a clustering result for a data set. The CDV index is composed of three major factors, including a statistically calculated external diameter factor, a restorer factor to reduce the effect of data dimension, and a number of clusters related punishment factor. With the calculation of the product of the three factors under various number of clusters settings, the best clustering result for some number of clusters setting is able to be found by searching for the minimum value of CDV curve. In the empirical experiments presented in this research, K-Means clustering method is chosen for its simplicity and execution speed. For the presentation of the effectiveness and superiority of the CDV index in the experiments, several traditional cluster validity indexes were implemented as the control group of experiments, including DI, DBI, ADI, and the most effective PBM index in recent years. The data sets of the experiments are also carefully selected to justify the generalization of CDV index, including three real world data sets and three artificial data sets which are the simulation of real world data distribution. These data sets are all tested to present the superior features of CDV index.
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