Output details
11 - Computer Science and Informatics
Robert Gordon University
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Article title
Mining Recurring Concepts in a Dynamic Feature Space
Type
D - Journal article
Title of journal
IEEE Transactions on Neural Networks and Learning Systems
Article number
-
Volume number
PP
Issue number
99
First page of article
1
ISSN of journal
2162-2388
Year of publication
2013
URL
-
Number of additional authors
3
Additional information
The paper makes an important contribution to the data mining research by dynamically changing the feature space according to the user’s context. The thorough experimental study shows that the proposed technique is the fastest to recover when changes in the underlying concept generating the streaming data recurs, setting a benchmark in this research area. Parallelisation and high performance computing approaches have been used to address the Big Data problems; however, the technique provided in this research provides a novel and significant approach to Big Data analytics.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
0
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-