Mining from distributed and abstracted data

Xiaofeng Zhang*, Kwok Wai CHEUNG, Yunming Ye

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)167-176
Number of pages10
JournalWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Issue number5
Publication statusPublished - 1 Sept 2016

Scopus Subject Areas

  • Computer Science(all)


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