Novel clustering validity function based on the geometry structure of data

作者：Zhao Heng^*; Zhang Gao Yu; Hu Hai Hong

来源：Systems Engineering and Electronics, 2007, 29(1): 96-98.

• Clustering compactness
• Clustering separation
• Clustering validity

Many clustering validity functions have been proposed, especially those based on the geometrical structure of data set, such as Dunn';s index and Xie-Beni index. Xie-Beni index decreases with the number of partitions increasing. It is difficult to choose the optimal partition of data when the number of clusters is large. From the point of view of the compactness and the separation of clustering, a novel clustering validity function is proposed, which is based on the improved Huber Γ statistic combined with the separation of clustering. The function has the only maximum with the number of clusters increasing. The experiment indicates that the function is simply, precise and robust, can be used as the optimal index for choosing the optimal partition of data.

• 出版日期2007
• 单位西安电子科技大学