Potential-Based Hierarchical Clustering

When performing hierarchical clustering, some metric must be used to determine the similarity between pairs of clusters. Traditional similarity metrics either can only deal with simple shapes or are very sensitive to outliers. We propose two potential-based similarity metrics, APES and AMAPES, inspired by the concept of electric potential in physics. The main features of these metrics are: the first, they have strong anti-jamming capability; the second, they are capable of finding clusters of complex irregular shapes.

• Type: Conference
• Year: 2002
• Where: ICPR
• Authors: Shuming Shi, Guangwen Yang, Dingxing Wang, Weimin Zheng
• Keywords: Complex Irregular Shapes, Computer Vision, ICPR 2002, Potential-based Similarity Metrics, Traditional Similarity Metrics