Hsiao-Dong Chiang, Tian-Shi Xu, Xian-Long Lv, and Na Dong
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K-means has been widely used in solving a wide range of clustering problems arising in engineering and industrial applications, but it still suffers from several issues. To address these issues, a hierarchical K-means method enhanced by Trust-Tech (H-KTT) is presented in this paper. The proposed H-KTT method is composed of two stages. The first stage of H-KTT is a hierarchical K-means (H-K-means) method for enhancing K-means with better initial points. Second, the H-K-means method is further enhanced to find multiple high-quality clustering results by the Trust-Tech methodology. The H-KTT method was evaluated on several test datasets including the clustering of Automatic Meter Reading (AMR), popular in power grids, with promising results. In particular, the evaluation results indicate that the proposed H-KTT method can significantly improve both the quality and stability of the clustering results by the K-means method. Furthermore, while the K-means gives stochastic clustering results, the proposed H-KTT method usually gives deterministic clustering results.