High Dimensional Clustering: A Strongly Connected Component Clustering Solution (SCCC)

High dimensional data is often challenging to cluster due to the curse of dimensionality leading to challenges in identifying clusters. The key challenge in high dimensional clustering is to develop a solution that identifies clusters which are as complete as they can be, while not merging well-separated clusters. We propose core points which represent local compact regions. The strongly connected component from the k-nearest neighbor graph of core points provides for a group of points that are strongly mutually connected. These mutually connected regions represent the core structure of the clusters. Our empirical analysis and experimental results present the rationale behind our solution and validate the goodness of the clusters against the state of the art high dimensional clustering algorithms. The novelty of our solution is to use the concept of reverse nearest neighbors to generate natural clusters in high dimensions.