An optimal data partitioning in parallel & distributed implementation of clustering algorithms is a necessary computation as it ensures independent task completion, fair distribution, less number of affected points and better & faster merging. Though partitioning using Kd Tree is being conventionally used in academia, it suffers from performance drenches and bias (non equal distribution) as dimensionality of data increases and hence is not suitable for practical use in industry where dimensionality can be of order of 100s to 1000s. To address these issues we propose two new partitioning techniques using existing mathematical models & study their feasibility, performance (bias and partitioning speed) & possible variants in choosing initial seeds. First method uses an n dimensional hashed grid based approach which is based on mapping the points in space to a set of cubes which hashes the points. Second method uses a tree of voronoi planes where each plane corresponds to a partition. We found that grid based approach was computationally impractical, while using a tree of voronoi planes (using scalable K-Means++ initial seeds) drastically outperformed the Kd-tree tree method as dimensionality increased.