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WIREs Data Mining Knowl Discov
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Wiley Interdisciplinary Reviews:
WIREs Data Mining and Knowledge Discovery
Volume 11 Issue 3 (May 2021)
Page 0 - 0

Overviews

Data stream analysis: Foundations, major tasks and tools
Published Online: Mar 02 2021
DOI: 10.1002/widm.1405
Data Stream Mining
Abstract Full article on Wiley Online Library:   HTML | PDF
Review on publicly available datasets for educational data mining
Published Online: Feb 19 2021
DOI: 10.1002/widm.1403
This paper presents a brief, yet informative review on publicly available datasets, open‐source code and models, and integration systems that perform comparative analysis. The study comes as an ingredient for future progress in Educational Data Mining
Abstract Full article on Wiley Online Library:   HTML | PDF
Introduction to neural network‐based question answering over knowledge graphs
Published Online: Feb 01 2021
DOI: 10.1002/widm.1389
Categorizations of the discussed models of question answering over knowledge graph.
Abstract Full article on Wiley Online Library:   HTML | PDF

Advanced Reviews

Time series analysis via network science: Concepts and algorithms
Published Online: Mar 01 2021
DOI: 10.1002/widm.1404
Mapping univariate and multivariate time series into complex networks. Comprehensive overview of existing literature.
Abstract Full article on Wiley Online Library:   HTML | PDF
Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review
Published Online: Jan 18 2021
DOI: 10.1002/widm.1402
A common framework of fuzzy rough set theory based feature selection approaches.
Abstract Full article on Wiley Online Library:   HTML | PDF
Privacy preserving classification over differentially private data
Published Online: Dec 13 2020
DOI: 10.1002/widm.1399
Privacy preserving classification over differentially private data.
Abstract Full article on Wiley Online Library:   HTML | PDF
Predicting the ratings of Amazon products using Big Data
Published Online: Dec 12 2020
DOI: 10.1002/widm.1400
Big Data is non‐expensive frameworks that can store a large variety of dataset and process it as parallel and disturbed systems. The paper aims to apply several machine learning models to the massive dataset in the area of e‐commerce from Amazon to analyze and predict “ratings” and to recommend products.
Abstract Full article on Wiley Online Library:   HTML | PDF

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