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WIREs Data Mining Knowl Discov
Impact Factor: 7.250
Wiley Interdisciplinary Reviews:
WIREs Data Mining and Knowledge Discovery
Volume 11 Issue 4 (July 2021)
Page 0 - 0

Overview

Table understanding approaches for extracting knowledge from heterogeneous tables
Published Online: Mar 28 2021
DOI: 10.1002/widm.1407
The “table understanding problem” consists in the automatic extraction of meaningful information from tables that can be exploited for data integration, data exchange and for answering queries. In this survey we compare the main approaches proposed in the last 15 years.
Abstract Full article on Wiley Online Library:   HTML | PDF

Advanced Reviews

Big data analytics in single‐cell transcriptomics: Five grand opportunities
Published Online: May 11 2021
DOI: 10.1002/widm.1414
Schematic representation of the emergent opportunities for big data analytics in the field of single‐cell transcriptomics.
Abstract Full article on Wiley Online Library:   HTML | PDF
A 2021 update on cancer image analytics with deep learning
Published Online: Apr 22 2021
DOI: 10.1002/widm.1410
An overview of calibrated deep learning model for cancer medical image analysis.
Abstract Full article on Wiley Online Library:   HTML | PDF
Data mining for energy systems: Review and prospect
Published Online: Mar 24 2021
DOI: 10.1002/widm.1406
This paper reviews some machine learning techniques for power big data mining, such as deep learning, transfer learning, randomized learning, granular computing and multi‐source data fusion. Some typical applications, such as load forecasting and modelling, integrated power and transportation system, and electricity market forecasting, are discussed.
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Focus Article

Incorporating domain ontology information into clustering in heterogeneous networks
Published Online: May 10 2021
DOI: 10.1002/widm.1413
A general framework for the clustering of heterogeneous data involves six steps: collecting raw data, cleaning data from heterogeneous sources, gathering information from all aspects, determining the aim of clustering, proposing clustering methods, and analyzing clustering results. Among this procedure, domain knowledge could be incorporated into clustering via ontology and the role of links should be much valued in heterogeneous networks.
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Software Focus

Automatic segmentation to characterize anthropometric parameters and cardiovascular indicators in children
Published Online: May 03 2021
DOI: 10.1002/widm.1411
Scheme of different step and substeps of data processing. (1) Data processing, (2) dimensional reduction: Principal component analysis (PCA) and clustering analysis, (3) modeling processing: Model selection, training, testing, and implementation, and (4) prediction result through new data sample.
Abstract Full article on Wiley Online Library:   HTML | PDF

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