Determining the number of clusters k-Means is the most populer problem among data mining researchers because of the difficulty to determining information from the data a priori so that the results cluster un optimal and to be quickly trapped into local minimums. Automatic clustering method with evolutionary computation (EC) approach can solve the k-Means problem. The automatic clustering differential evolution (ACDE) method is one of the most popular methods of the EC approach because it can handle high-dimensional data and improve k-Means drafting performance with low cluster validity values. However, the process of determining k activation threshold on ACDE is still dependent on user considerations, so that the process of determining the number of k-Means clusters is not yet efficient. In this study, the ACDE problem will be improved using the u-control chart (UCC) method, which is proven to be efficiently used to solve k-Means problems automatically. The proposed method is evaluated using the state-of-the-art datasets such as synthetic data and real data (iris, glass, wine, vowel, ruspini) from UCI repository machine learning and using davies bouldin index (DBI) and cosine similarity measure (CS) as an evaluation method. The results of this study indicate that the UCC method has successfully improved the k-Means method with the lowest objective values of DBI and CS of 0.470 and 0.577 respectively. The lowest objective value of DBI and CS is the best method. The proposed method has high performance when compared with other current methods such as genetic clustering for unknown k (GCUK), dynamic clustering pso (DCPSO) and automatic clustering approach based on differential evolution algorithm combining with k-Means for crisp clustering (ACDE) for almost all DBI and CS evaluations. It can be concluded that the UCC method is able to correct the weakness of the ACDE method on determining the number of k-Means clusters by automatically determining k activation threshold
Penggabungan Metode U-Control Chart dan Metode Automatic Clustering Differential Evolution untuk Penentuan Jumlah Klaster pada Metode K-Means
