On a Distributed Approach for Density-based Clustering

TL;DR

This paper proposes a novel distributed density-based clustering method that reduces communication costs and enhances global model quality by considering local cluster shapes, addressing challenges of distributed, heterogeneous data.

Contribution

It introduces a new distributed clustering algorithm that improves model quality and reduces data exchange overhead by incorporating local cluster shape information.

Findings

Preliminary results show promising clustering quality.

The method reduces communication overhead significantly.

Improves global model accuracy by considering local cluster shapes.

Abstract

Efficient extraction of useful knowledge from these data is still a challenge, mainly when the data is distributed, heterogeneous and of different quality depending on its corresponding local infrastructure. To reduce the overhead cost, most of the existing distributed clustering approaches generate global models by aggregating local results obtained on each individual node. The complexity and quality of solutions depend highly on the quality of the aggregation. In this respect, we proposed for distributed density-based clustering that both reduces the communication overheads due to the data exchange and improves the quality of the global models by considering the shapes of local clusters. From preliminary results we show that this algorithm is very promising.