The automatic clustering differential evolution (ACDE) is one of the
clustering methods that are able to determine the cluster number automatically.
However, ACDE still makes use of the manual strategy to determine k activation
threshold thereby affecting its performance. In this study, the ACDE problem
will be ameliorated using the u-control chart (UCC) then the cluster number
generated from ACDE will be fed to k-means. The performance of the proposed
method was tested using six public datasets from the UCI repository about
academic efficiency (AE) and evaluated with Davies Bouldin Index (DBI) and
Cosine Similarity (CS) measure. The results show that the proposed method
yields excellent performance compared to prior researches.