Density-based clustering of data streams at multiple resolutions
ACM Transactions on Knowledge Discovery from Data (TKDD)
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CLOUD '09 Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing
COMET: event-driven clustering over multiple evolving streams
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Granularity adaptive density estimation and on demand clustering of concept-drifting data streams
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
SIC-means: a semi-fuzzy approach for clustering data streams using c-means
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
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In the data stream environment, the patterns generated by the mining techniques are usually distinct at different time because of the evolution of data. In order to deal with various types of multiple data streams and to support flexible mining requirements, we devise in this paper a Clustering on Demand framework, abbreviated as COD framework, to dynamically cluster multiple data streams. While providing a general framework of clustering on multiple data streams, the COD framework has two major features, namely one data scan for online statistics collection and compact multi-resolution approximations, which are designed to address, respectively, the time and the space constraints in a data stream environment. Furthermore, with the multi-resolution approximations of data streams, flexible clustering demands can be supported.