Hierarchical aggregation approach for distributed clustering of spatial datasets
Bendechache, MalikaORCID: 0000-0003-0069-1860, Le-Khac, Nhien-An and Kechadi, M-TaharORCID: 0000-0002-0176-6281
(2017)
Hierarchical aggregation approach for distributed clustering of spatial datasets.
In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 12-15 Dec 2016, Barcelona, Spain.
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.
Metadata
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Conference
Refereed:
Yes
Uncontrolled Keywords:
Big Data; spatial data; clustering; distributed mining; data analysis; k-means; DBSCAN; balance vector