In this paper, we propose a dynamic clustering algorithm based on sensor node frequency for object tracking sensor networks, and we improved apriori algorithm for mining object move association rules, make it applies to the object tracking sensor networks , which is able to extract data regarding the sensors’ patterns, let dig out the item make the path of moving objects can be found. The main goal of determine node frequency is to use them to K-mean clustering and appropriate association item, that will improve the network live time and forecast accuracy. Several experimental studies have been conducted to evaluate our proposed dynamic clustering method for clustering object tracking sensor networks.