Authors:
Ching-Ming Chao
1
and
Guan-Lin Chao
2
Affiliations:
1
Soochow University, Taiwan
;
2
National Taiwan University, Taiwan
Keyword(s):
Data Mining, Data Streams, Clustering, Ubiquitous Data Mining, Ubiquitous Data Stream Mining.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Software Agents and Internet Computing
;
Ubiquitous Computing
Abstract:
Data stream mining has attracted much research attention from the data mining community. With the advance of wireless networks and mobile devices, the concept of ubiquitous data mining has been proposed. However, mobile devices are resource-constrained, which makes data stream mining a greater challenge. In this paper, we propose the RA-HCluster algorithm that can be used in mobile devices for clustering stream data. It adapts algorithm settings and compresses stream data based on currently available resources, so that mobile devices can continue with clustering at acceptable accuracy even under low memory resources. Experimental results show that not only is RA-HCluster more accurate than RA-VFKM, it is able to maintain a low and stable memory usage.