Parallel Hierarchical Clustering on Shared Memory Platforms

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Hierarchical clustering is a powerful technique that offers several advantages over traditional partitional clustering techniques, including its non-parametric nature and its ability to elucidate the overall structure of a dataset. Hierarchical clustering has many advantages over traditional clustering algorithms like k-means, but it suffers from higher computational costs and a less obvious parallel structure. Thus, in order to scale this technique up to larger datasets, the authors present SHRINK, a novel shared-memory algorithm for single linkage hierarchical clustering based on merging the solutions from overlapping sub-problems.