A parallel sampling based clustering

TL;DR

This paper proposes parallel and serial subclustering schemes to improve the efficiency of data clustering by dividing datasets into smaller parts, enabling faster processing with minimal accuracy loss.

Contribution

Introduces two novel subclustering schemes that enable parallel processing, significantly reducing clustering time while maintaining accuracy.

Findings

Parallel execution is much faster than traditional methods.

Error introduced by subclustering is very low.

Both methods outperform original algorithms in speed.

Abstract

The problem of automatically clustering data is an age old problem. People have created numerous algorithms to tackle this problem. The execution time of any of this algorithm grows with the number of input points and the number of cluster centers required. To reduce the number of input points we could average the points locally and use the means or the local centers as the input for clustering. However since the required number of local centers is very high, running the clustering algorithm on the entire dataset to obtain these representational points is very time consuming. To remedy this problem, in this paper we are proposing two subclustering schemes where by we subdivide the dataset into smaller sets and run the clustering algorithm on the smaller datasets to obtain the required number of datapoints to run our clustering algorithm with. As we are subdividing the given dataset, we could run clustering algorithm on each smaller piece of the dataset in parallel. We found that both parallel and serial execution of this method to be much faster than the original clustering algorithm and error in running the clustering algorithm on a reduced set to be very less.