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WIREs Data Mining Knowl Discov
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Wiley Interdisciplinary Reviews:
WIREs Data Mining and Knowledge Discovery
Volume 10 Issue 4 (July 2020)
Page 0 - 0

Overview

Temporal association rule mining: An overview considering the time variable as an integral or implied component
Published Online: Apr 27 2020
DOI: 10.1002/widm.1367
Overview taxonomy on temporal association rule mining by considering the time variable as an integral or implied component.
Abstract Full article on Wiley Online Library:   HTML | PDF

Advanced Reviews

Continuous authentication using biometrics: An advanced review
Published Online: Mar 24 2020
DOI: 10.1002/widm.1365
Flowchart of a continuous authentication system. An initial log‐in operation retrieves or creates the authorized user's template, which is later used to continuously verify the identity of the logged user using recently acquired biometric samples.
Abstract Full article on Wiley Online Library:   HTML | PDF
A survey and taxonomy of adversarial neural networks for text‐to‐image synthesis
Published Online: Feb 19 2020
DOI: 10.1002/widm.1345
A visual summary of the generative adversarial network (GAN) based text‐to‐image synthesis process, and the summary of GAN‐based frameworks/methods reviewed in the survey.
Abstract Full article on Wiley Online Library:   HTML | PDF
On the determinants of Uber accessibility and its spatial distribution: Evidence from Uber in Philadelphia
Published Online: Feb 11 2020
DOI: 10.1002/widm.1362
Impact of socioeconomics, and demographic factors and transportation infrastructure on ride‐sourcing platforms.
Abstract Full article on Wiley Online Library:   HTML | PDF

Focus Articles

Designing and deploying insurance recommender systems using machine learning
Published Online: May 02 2020
DOI: 10.1002/widm.1363
Designing and Deploying Insurance Recommender Systems using Machine Learning.
Abstract Full article on Wiley Online Library:   HTML | PDF
Online streaming feature selection with incremental feature grouping
Published Online: Mar 17 2020
DOI: 10.1002/widm.1364
We address the extremely high‐dimensionality challenge of streaming data observed in the form of features stream and propose a novel online feature grouping technique to resolve the scalability issue of the streaming feature selection.
Abstract Full article on Wiley Online Library:   HTML | PDF

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