Abstract:  In cluster analysis, especially cluster in an optimization process, one of the dramatically affect is the similarity measure employed in the clustering criterion function. By far, all proposed cluster methods have to assume connection among the information objects that applied on. Similarity between every pair objects should be computed, there are two choices which defined as explicitly or implicitly. Hence weather the structure of data can be described by the similarity measure correctly determines the effectiveness of a clustering algorithm. In addition, as one of important characters in human’s cognition, multi-granulation cognition plays a key role for data modeling. On account of from multi-perspective and multi-level to parse one problem, multi-granulation analysis can obtain more reasonable and more satisfied solutions. Through referencing human’s multi-granulation cognitive ability, in this paper, we introduced a novel similarity measure called whole-granulation similarity measure and apply this similarity measure into clustering criterion function to get a cluster algorithm called whole-granulation cluster algorithm in order to verify the rationalization of whole-granulation similarity measure. The traditional dissimilarity/similarity measure exercise only one single viewpoints, usually is the origin. More informative assessment of similarity could be achieved because whole-granulation takes all sides into consideration. As a leading partitional clustering technique, k-means is one of the most favorite algorithms to be used, because k-means is fast and easy to combine with other methods. Many research put forward the k-means through improve the heuristic function or combine with other method. This is an active aspect to do clustering research. Under this approach we introduce our measure method into cluster analysis through k-means algorithm as an initial testing. Experiments are conducted with five data sets are selected from UCI machine learning repository. Finally, compared whole-granulation cluster algorithm with two traditional cluster algorithms to verity the validity and proved the rationality of whole-granulation similarity measure at the same time. And the astringency experiment show that whole-granulation similarity measure have a strong performance as a way to measure similarity.