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
Enabling resource-awareness for in-network data processing in wireless sensor networks
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
Proceedings of the 2009 International Conference on Hybrid Information Technology
Mobile video user revisit analysis based on multi-day visiting patterns
ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
Resource aware distributed knowledge discovery
Ubiquitous knowledge discovery
L2GClust: local-to-global clustering of stream sources
Proceedings of the 2011 ACM Symposium on Applied Computing
Resource-aware ECG analysis on mobile devices
Proceedings of the 2011 ACM Symposium on Applied Computing
Resource aware distributed knowledge discovery
Ubiquitous knowledge discovery
Techniques for improving filters in power grid contingency analysis
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Bayesian networks to predict data mining algorithm behavior in ubiquitous computing environments
MSM'10/MUSE'10 Proceedings of the 2010 international conference on Analysis of social media and ubiquitous data
Homogeneous and heterogeneous distributed classification for pocket data mining
Transactions on Large-Scale Data- and Knowledge-Centered Systems V
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Mining data streams is a field of increase interest due to the importance of its applications and dissemination of data stream generators. Most of the streaming techniques developed so far have not addressed the need of resource-aware computing in data stream analysis. The fact that streaming information is often generated or received onboard resource-constrained computational devices such as sensors 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 stream clustering algorithm that can adapt to the changing availability of different resources. The experimental results showed the applicability of the framework and the algorithm in terms of resource-awareness and accuracy.