Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format
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Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format
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Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format Example of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery format
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Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery — Template for authors

Publisher: Wiley
Categories Rank Trend in last 3 yrs
Computer Science (all) #8 of 226 up up by 6 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 154 Published Papers | 2083 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 17/07/2020
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Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

4.476

76% from 2018

Impact factor for Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery from 2016 - 2019
Year Value
2019 4.476
2018 2.541
2017 1.939
2016 2.111
graph view Graph view
table view Table view

13.5

61% from 2019

CiteRatio for Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery from 2016 - 2020
Year Value
2020 13.5
2019 8.4
2018 5.6
2017 6.1
2016 6.4
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 76% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 61% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.506

3% from 2019

SJR for Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery from 2016 - 2020
Year Value
2020 1.506
2019 1.465
2018 0.928
2017 0.971
2016 0.782
graph view Graph view
table view Table view

4.893

13% from 2019

SNIP for Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery from 2016 - 2020
Year Value
2020 4.893
2019 4.339
2018 2.669
2017 2.128
2016 2.156
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 3% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 13% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

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Wiley

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Approved by publishing and review experts on SciSpace, this template is built as per for Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery formatting guidelines as mentioned in Wiley author instructions. The current version was created on 17 Jul 2020 and has been used by 133 authors to write and format their manuscripts to this journal.

Computer Science

i
Last updated on
17 Jul 2020
i
ISSN
1942-4787
i
Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
apa
i
Citation Type
Numbered
[25]
i
Bibliography Example
Beenakker, C.W.J. (2006) Specular andreev reflection in graphene.Phys. Rev. Lett., 97 (6), 067 007. URL 10.1103/PhysRevLett.97.067007.

Top papers written in this journal

Journal Article DOI: 10.1002/WIDM.8
Classification and regression trees
Wei-Yin Loh1

Abstract:

Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classif... Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an introduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 14-23 DOI: 10.1002/widm.8 This article is categorized under: Technologies > Classification Technologies > Machine Learning Technologies > Prediction Technologies > Statistical Fundamentals read more read less

Topics:

Decision tree learning (59%)59% related to the paper, Logistic model tree (54%)54% related to the paper, Recursive partitioning (53%)53% related to the paper, Variables (52%)52% related to the paper, Incremental decision tree (51%)51% related to the paper
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16,974 Citations
Journal Article DOI: 10.1002/WIDM.1249
Ensemble learning: A survey

Abstract:

Ensemble methods are considered the state‐of‐the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state‐of‐the‐a... Ensemble methods are considered the state‐of‐the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state‐of‐the‐art ensemble methods and discusses current challenges and trends in the field. read more read less

Topics:

Ensemble learning (77%)77% related to the paper, Boosting (machine learning) (65%)65% related to the paper, Ensemble forecasting (57%)57% related to the paper, Random forest (54%)54% related to the paper
1,381 Citations
Journal Article DOI: 10.1002/WIDM.1046
Experimental design
Xinwei Deng1

Abstract:

Maximizing data information requires careful selection, termed design, of the points at which data are observed. Experimental design is reviewed here for broad classes of data collection and analysis problems, including: fractioning techniques based on orthogonal arrays, Latin hypercube designs and their variants for computer... Maximizing data information requires careful selection, termed design, of the points at which data are observed. Experimental design is reviewed here for broad classes of data collection and analysis problems, including: fractioning techniques based on orthogonal arrays, Latin hypercube designs and their variants for computer experimentation, efficient design for data mining and machine learning applications, and sequential design for active learning. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc. read more read less

Topics:

Active learning (machine learning) (58%)58% related to the paper, Latin hypercube sampling (52%)52% related to the paper, Orthogonal array (50%)50% related to the paper
1,025 Citations
Journal Article DOI: 10.1002/WIDM.53
Algorithms for hierarchical clustering: an overview
Fionn Murtagh1, Fionn Murtagh2, Pedro Contreras2

Abstract:

We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally, we describe a r... We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally, we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid-based algorithm. This review adds to the earlier version, Murtagh F, Contreras P. Algorithms for hierarchical clustering: an overview, Wiley Interdiscip Rev: Data Mining Knowl Discov 2012, 2, 86–97. WIREs Data Mining Knowl Discov 2017, 7:e1219. doi: 10.1002/widm.1219 This article is categorized under: Algorithmic Development > Hierarchies and Trees Technologies > Classification Technologies > Structure Discovery and Clustering read more read less

Topics:

Hierarchical clustering (69%)69% related to the paper, Brown clustering (69%)69% related to the paper, Cluster analysis (65%)65% related to the paper
977 Citations
open accessOpen access Journal Article DOI: 10.1002/WIDM.1253
Deep learning for sentiment analysis: A survey
Lei Zhang1, Shuai Wang2, Bing Liu2

Abstract:

Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in re... Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years. This paper first gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis. read more read less

Topics:

Sentiment analysis (68%)68% related to the paper, Deep learning (59%)59% related to the paper, Artificial neural network (56%)56% related to the paper
917 Citations
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Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery that you can download at the end.

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13. What is Sherpa RoMEO Archiving Policy for Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery?

The 5 most common citation types in order of usage for Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery Endnote style according to Elsevier guidelines.

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