Abstract: A new algorithm, named adaptively determining the number of clusters (ADNC), has been proposed. By using ADNC, the optimal clustering number for Kmeans clustering, usually determined by human conjecture or manual try, can now be determined by computer in a self-adaptive way.ADNC typically is an iterative process including the adjustment of clustering number and the assessment of average standard deviation during the iteration. The adjustment will refer the assessment following the principle of gradient descent, namely, to get a better clustering number and to reduce the deviation in the same time. The optimal clustering number most likely locates at the point just before the deviation begins to oscillate. The clustering results will be perfectly reasonable with the clustering number decided by ADNC because the feature heterogeneity in a class will be reduced to the minimum. By the way, the concept of inappropriate clustering number, by using which the deviation may increase to the maximum, has been proposed as a try. It has been revealed by experiment that both the optimal and the inappropriate clustering numbers have practical significance to improve the clustering accuracy.

Key words: K-means, clustering number, self-adaptation