Premium
Mining from distributed and abstracted data
Author(s) -
Zhang Xiaofeng,
Cheung William K.,
Ye Yunming
Publication year - 2016
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1182
Subject(s) - computer science , data mining , cluster analysis , abstraction , data science , knowledge extraction , association rule learning , data stream mining , machine learning , philosophy , epistemology
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 This article is categorized under: Technologies > Computer Architectures for Data Mining Technologies > Structure Discovery and Clustering Technologies > Visualization