Hierarchical Aggregation Approach for Distributed clustering of spatial datasets
This work addresses the challenge of efficient clustering in distributed spatial data environments, though it appears incremental as it builds on known techniques with specific optimizations.
The paper tackles the problem of distributed clustering for spatial datasets by introducing a two-phase hierarchical aggregation approach, achieving improved performance in response time and memory efficiency compared to existing algorithms.
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