A Novel Approach for Improving Efficiency of Agglomerative Hierarchical Clustering For Numerical Data Set

Hierarchical clustering methods construct the clusters by recursively partitioning the instances in either a top-down or bottom-up fashion. These methods can be subdivided into Agglomerative hierarchical clustering and Divisive hierarchical clustering. The result of the hierarchical methods is a dendrogram, representing the nested grouping of objects and similarity levels at which groupings change. A clustering of the data objects is obtained by cutting the dendrogram at the desired similarity level. Single linkage method is based on similarity of two clusters that are most similar (closest) points in the different clusters. Complete linkage method based on similarity of two clusters that are least similar (most distant) points in the different clusters. Average linkage method based on average of pairwise proximity between points in the two clusters. In this paper we proposed an ensemble based technique to decide which methods is most suitable for a given dataset.