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Distributed clustering algorithm for spatial data mining

Bendechache, Malika orcid logoORCID: 0000-0003-0069-1860 and Kechadi, M-Tahar orcid logoORCID: 0000-0002-0176-6281 (2015) Distributed clustering algorithm for spatial data mining. In: 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM2015 ), 8-10 July 2015, Fuzhou, China.

Abstract
Distributed data mining techniques and mainly distributed clustering are widely used in the last decade because they deal with very large and heterogeneous datasets which cannot be gathered centrally. Current distributed clustering approaches are normally generating global models by aggregating local results that are obtained on each site. While this approach mines the datasets on their locations the aggregation phase is complex, which may produce incorrect and ambiguous global clusters and therefore incorrect knowledge. In this paper we propose a new clustering approach for very large spatial datasets that are heterogeneous and distributed. The approach is based on K-means Algorithm but it generates the number of global clusters dynamically. Moreover, this approach uses an elaborated aggregation phase. The aggregation phase is designed in such a way that the overall process is efficient in time and memory allocation. Preliminary results show that the proposed approach produces high quality results and scales up well. We also compared it to two popular clustering algorithms and show that this approach is much more efficient.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Spatial data; clustering;distributed mining; data analysis; k-mean
Subjects:Computer Science > Artificial intelligence
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM). . IEEE.
Publisher:IEEE
Official URL:http://dx.doi.org/10.1109/ICSDM.2015.7298026
Copyright Information:© 2015 The Authors
Funders:Science Foundation Ireland under Grant Number SFI/12/RC/2289.
ID Code:24622
Deposited On:16 Jun 2020 15:22 by Malika Bendechache . Last Modified 16 Jun 2020 15:22
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