A Study of Clustering Algorithm Based on Central Symmetry

• Main Authors: Chu-Hsuan Huang, 黃朱玄
• Other Authors: 吳俊霖
• Format: Others
• Language: en_US
• Online Access: http://ndltd.ncl.edu.tw/handle/03885624110982744605

碩士 === 國立中興大學 === 資訊科學系所 === 94 === K-means clustering is a very popular clustering technique which is widely used in numerous engineering and scientific disciplines such as image segmentation, pattern recognition and data mining. The similarity measure used in the conventional K-means algorithm is Euclidean distance. The symmetry-based K-means (SBKM) algorithm was proposed to cluster the data sets with the geometrical symmetrical structure, it used the “point symmetry distance” instead of “Euclidean distance” as the similarity measure. However, in practice, it does not work well in the case of where there is highly symmetry between the clusters. In this paper, a modified version of the SBKM algorithm is proposed to solve the above problem. We use a hybrid distance measure by the combination of the symmetry distance and the Euclidean distance. Several examples are used to demonstrate the robustness and effectiveness of the proposed algorithm.