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. For further resources related to this article, please visit the WIREs website.
|Number of pages||10|
|Journal||Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery|
|Publication status||Published - 1 Sep 2016|
Scopus Subject Areas
- Computer Science(all)