BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
ACM SIGMOD Record
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Distributed data stream classification for wireless sensor networks
Proceedings of the 2010 ACM Symposium on Applied Computing
Quality-driven resource-adaptive data stream mining?
ACM SIGKDD Explorations Newsletter
Interactive self-adaptive clutter-aware visualisation for mobile data mining
Journal of Computer and System Sciences
Self-configuring data mining for ubiquitous computing
Information Sciences: an International Journal
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Mining data streams is a field of increasing interest due to the importance of its applications and dissemination of data stream sources. Most of the streaming techniques developed so far have not addressed the need for resource-aware computing in data stream analysis. The fact that streaming information is often generated or received onboard resource-constrained computational devices such as sensor nodes and mobile devices motivates the need for resource-awareness in data stream processing systems. In this paper, we propose a generic framework that enables resource-awareness in streaming computation using algorithm granularity settings in order to change the resource consumption patterns periodically. This generic framework is applied to a novel threshold-based micro-clustering algorithm to test its validity and feasibility. We have termed this algorithm as RA-Cluster. RA-Custer is the first data stream clustering algorithm that can adapt to the changing availability of different resources. The experimental results show the applicability of the framework and the algorithm in terms of resource-awareness and accuracy.