Published Online: Nov 28 2019
DOI: 10.1002/widm.1344
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
WIREs Data Mining Knowl Discov
Impact Factor: 4.476
Wiley Interdisciplinary Reviews:
WIREs Data Mining and Knowledge Discovery
WIREs Data Mining and Knowledge Discovery
Volume 10 Issue 1
(January 2020)
Page 0 - 0
Editorial Commentary
Overviews
Published Online: Oct 24 2019
DOI: 10.1002/widm.1334
Visual illustration of algorithmic differentiation and overview of the topics covered in this paper.
Published Online: Jul 11 2019
DOI: 10.1002/widm.1328
The techniques of SDA can be categorized into unimodal and multimodal for feature selection and sentiment classification. Based on the characteristics of education big‐data, the applications of these techniques focus on traditional classroom and e‐learning situations.
Advanced Reviews
Published Online: Oct 15 2019
DOI: 10.1002/widm.1338
The architecture of proposed churn prediction model MOC‐ACO‐Miner.
Published Online: Oct 01 2019
DOI: 10.1002/widm.1335
Solving a problem on a (small) data summarization (core‐set) yields provably good approximation to the optimal solution of the original (big) data.
Published Online: Aug 28 2019
DOI: 10.1002/widm.1333
Sentiment Analysis Methods, Tools and Literature Survey.
Published Online: Sep 30 2018
DOI: 10.1002/widm.1287
Data Mining and Foreign Language Learning: the key to a more effective education for the future?
Focus Articles
Published Online: Nov 03 2019
DOI: 10.1002/widm.1342
The hierarchical diagram of the social media ecosystem at a news outlet.
Published Online: Oct 17 2019
DOI: 10.1002/widm.1340
Paper Organization
Published Online: Oct 15 2019
DOI: 10.1002/widm.1336
Fault detection procedure of the RMU based on ATNPE. ATNPE has the ability of adapting new samples and SPE statistic limit responded to changes in statistical characteristics of monitoring data. It can effectively reduce the false alarm rate and has the same fault detection rate compared with existing model in RMU cable joint fault detection.