Determining the cluster number in k-means is problematic since it affects the quality of cluster for numerous applications in the data mining.The automatic clustering differential evolution (ACDE) is one of the most used clustering methods that are able to determine the cluster number automatically.However, ACDE still makes use of the manual strategy to determine a value k activation threshold thereby affecting its performance.In this study, the u-control chart (UCC) method use to tackle the ACDE method problem.UCC method used for the initial step to get a value of the variables sought before initialization of the variable vector on ACDE.The UCC is a method from statistical process control (SPC) field which has proved to be effective in solving the problem of management control attributes.The performance of the proposed method was tested using seven public datasets from the UCI repository and Clustering basic benchmark repository and evaluated with Davies Bouldin Index (DBI) and CS measure.The results show that, the proposed method yields excellent performance compared to prior researches for most datasets with optimal cluster number yet lowest DBI and CS measure.It can be concluded that the UCC method is able to determine k activation threshold in ACDE that caused effective determination of the cluster number for k-means clustering.