An Efficient Hybrid Hierarchical Agglomerative Clustering (HHAC) Technique for Partitioning Large Data Sets
In this paper, an efficient Hybrid Hierarchical Agglomerative Clustering (HHAC) technique is proposed for effective clustering and prototype selection for pattern classification. It uses the characteristics of both partitional (an incremental scheme) and Hierarchical Agglomerative Clustering (HAC) schemes. Initially, an incremental, partitional clustering algorithm – leader is used for finding the subgroups/subclusters. It reduces the time and space requirements incurred in the formation of the subclusters using the conventional hierarchical agglomerative schemes or other methods. Further, only the subcluster representatives are merged to get a required number of clusters using a hierarchical agglomerative scheme which now requires less space and time when compared to that of using it on the entire training set. Thus, this hybrid scheme would be suitable for clustering large data sets and we can get a hierarchical structure consisting of clusters and subclusters. The subcluster representatives of a cluster can also handle its arbitrary/non-spherical shape. The experimental results (Classification Accuracy (CA) using the prototypes obtained and the computation time) of the proposed algorithm are promising.
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