Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs

Abstract

The Automatic Clustering using Differential Evolution (ACDE) is one of the grouping methods capable of automatically determining the number of the cluster. However, ACDE continues making use of the strategy manual to determine the activation threshold of k, which affects its performance. In this study, the problem of ACDE is enhanced using the U Control Chart (UCC). The performance of the proposed method was tested using five data sets from the National Administrative Department of Statistics (DANE - Departamento Administrativo Nacional de Estadísticas) and the Ministry of Commerce, Industry, and Tourism of Colombia for the innovative capacity of Small and Medium-sized Enterprises (SMEs) and were assessed by the Davies Bouldin Index (DBI) and the Cosine Similarity (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.