Potential based similarity metrics for implementing hierarchical clustering (International Journal of Engineering and Computer Science, 2015)
The main aim of the data mining process is to extract information from a large data set and transform it into an understandable form for further use. Clustering is important in data analysis and data mining applications. It is the task of grouping a set of objects so that objects in the same group are more similar to each other than to those in other groups (clusters). A prominent clustering is hierarchical clustering. Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. 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. Potential - based similarity metrics, Average potential energy similarity metric and Average maximal potential energy similarity metric have special features like strong anti-jamming capability and they are capable of finding clusters of complex irregular shapes.