Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Mining from distributed and abstracted data

Language

English

Abstract

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.

Publication Date

10-2016

Source Publication Title

Data Mining and Knowledge Discovery

Volume

6

Issue

5

Start Page

167

End Page

176

Publisher

Springer Verlag

Peer Reviewed

1

DOI

10.1002/widm.1182

Link to Publisher's Edition

http://dx.doi.org/10.1002/widm.1182

ISSN (print)

13845810

ISSN (electronic)

1573756X

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