Hierarchical Aggregation Approach for Distributed clustering of spatial datasets

TL;DR

This paper introduces a hierarchical distributed clustering method for spatial datasets that efficiently combines local clusters into accurate global clusters, outperforming existing algorithms in speed and memory use.

Contribution

The paper proposes a novel two-phase distributed clustering approach with an efficient aggregation technique for spatial data.

Findings

Outperforms existing clustering algorithms in experiments

Produces compact and accurate global clusters

Efficient in response time and memory usage

Abstract

In this paper, we present a new approach of distributed clustering for spatial datasets, based on an innovative and efficient aggregation technique. This distributed approach consists of two phases: 1) local clustering phase, where each node performs a clustering on its local data, 2) aggregation phase, where the local clusters are aggregated to produce global clusters. This approach is characterised by the fact that the local clusters are represented in a simple and efficient way. And The aggregation phase is designed in such a way that the final clusters are compact and accurate while the overall process is efficient in both response time and memory allocation. We evaluated the approach with different datasets and compared it to well-known clustering techniques. The experimental results show that our approach is very promising and outperforms all those algorithms