Nowadays information is generated and gathered from distributed streaming data sources, stressing communications and computing infrastructure, making it hard to transmit, compute, and store. Knowledge discovery from ubiquitous data streams has become a major goal for all sorts of applications, mostly based on unsupervised techniques such as clustering. Two subproblems exist: clustering streaming data observations and clustering streaming data sources. The former searches for dense regions of the data space, identifying hot spots where data sources tend to produce data, while the latter finds groups of sources that behave similarly over time. In order to better assess the current status of this topic, this article presents a thorough review on distributed algorithms addressing either of the subproblems. We characterize clustering algorithms for ubiquitous data streams, discussing advantages and disadvantages of distributed procedures. Overall, distributed stream clustering methods improve communication ratios, processing speed, and resources consumption, while achieving similar clustering validity as the centralized counterparts. WIREs Data Mining Knowl Discov 2014, 4:38–54. doi: 10.1002/widm.1109
Conflict of interest: The authors have declared no conflicts of interest for this article.
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