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An optimal data partitioning in parallel & distributed implementation ofclustering algorithms is a necessary computation as it ensures independent taskcompletion, fair distribution, less number of affected points and better &faster merging. Though partitioning using Kd Tree is being conventionally usedin academia, it suffers from performance drenches and bias (non equaldistribution) as dimensionality of data increases and hence is not suitable forpractical use in industry where dimensionality can be of order of 100s to1000s. To address these issues we propose two new partitioning techniques usingexisting mathematical models & study their feasibility, performance (bias andpartitioning speed) & possible variants in choosing initial seeds. First methoduses an n dimensional hashed grid based approach which is based on mapping thepoints in space to a set of cubes which hashes the points. Second method uses atree of voronoi planes where each plane corresponds to a partition. We foundthat grid based approach was computationally impractical, while using a tree ofvoronoi planes (using scalable K-Means++ initial seeds) drasticallyoutperformed the Kd-tree tree method as dimensionality increased.

An Efficient Method of Partitioning High Volumes of Multidimensional Data for Parallel Clustering Algorithms