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WIREs Data Mining Knowl Discov
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Mining from distributed and abstracted data

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Discovering global knowledge from distributed data sources is challenging as there exist several practical concerns such as bandwidth limitation and data privacy. By appropriately abstracting distributed data, various global data mining tasks could still be implemented on the basis of local data abstractions. This article reviews existing techniques related to distributed data mining in abstraction‐based data mining. It then discusses open research challenges on mining tasks performed on distributed and abstracted data, describes how global data models (clustering and manifold discovery) could be learnt based on local data models, and points out future research directions. WIREs Data Mining Knowl Discov 2016, 6:167–176. doi: 10.1002/widm.1182

The effect of the number of local components on the global cluster model obtained. A global cluster model with three components is learned from a data set generated using a GMM with seven components: (a) 9, (b) 15, and (c) 49 local components.
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Manifold discovery results on the oil flow data (, annular; , homogeneous; , stratified). The results are normalized into [−1,1] and visualized on a two‐dimensional data space. GTM learnt on (a) original data, (b) 20 local components per source, and (c) 50 local components per source.
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Technologies > Computer Architectures for Data Mining
Technologies > Structure Discovery and Clustering
Technologies > Visualization

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