Enhancing grid-density based clustering for high dimensional data

We propose an enhanced grid-density based approach for clustering high dimensional data. Our technique takes objects (or points) as atomic units in which the size requirement to cells is waived without losing clustering accuracy. For efficiency, a new partitioning is developed to make the number of cells smoothly adjustable; a concept of the ith-order neighbors is defined for avoiding considering the exponential number of neighboring cells; and a novel density compensation is proposed for improving the clustering accuracy and quality. We experimentally evaluate our approach and demonstrate that our algorithm significantly improves the clustering accuracy and quality.