Combination Clustering Evaluation Research on Different Data Distribution Patterns
Yu Chen Song, Xiao Liang Jia, Hai-Dong Meng · Advanced materials research · 2013
Clustering analysis, as a practical data mining method, has wide-ranging applications in many fields. But because of different original data resources, clustering results of different data distribution patterns and applicable clustering evaluation methods are different from each other. Aiming at different data distribution patterns, only reasonable clustering evaluation methods can achieve a better recognition of different clustering results for realizing the application value of clustering technology. In this paper, the combination clustering evaluation model is constructed form three angles, through clustering experiment of different artificial simulated data distribution patterns, comparative analysis draw a conclusion that the combination clustering evaluation model constructed is reasonable, and according to applicable clustering and clustering evaluation methods based on different data distribution patterns, the optimization clustering process is constructed for improving the effectiveness and interpretability of different clustering results.