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Trends and Applications in Knowledge Discovery and Data Mining

Overview of attention for book
Cover of 'Trends and Applications in Knowledge Discovery and Data Mining'

Table of Contents

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    Book Overview
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    Chapter 1 Matching Biomedical Ontologies with Compact Evolutionary Algorithm
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    Chapter 2 Towards Expert Preference on Academic Article Recommendation Using Bibliometric Networks
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    Chapter 3 Research in Progress: Explaining the Moderating Effect of Massively Multiplayer Online (MMO) Games on the Relationship Between Flow and Game Addiction Using Literature-Based Discovery (LBD)
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    Chapter 4 A Network Approach for Mapping and Classifying Shared Terminologies Between Disparate Literatures in the Social Sciences
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    Chapter 5 Towards Creating a New Triple Store for Literature-Based Discovery
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    Chapter 6 Who Is Who in Literature-Based Discovery: Preliminary Analysis
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    Chapter 7 Adversarial Deep Factorization for Recommender Systems
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    Chapter 8 Detection of Spammers Using Modified Diffusion Convolution Neural Network
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    Chapter 9 Dynamics of Online Toxicity in the Asia-Pacific Region
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    Chapter 10 Predictive K-means with Local Models
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    Chapter 11 Automatic Detection of Sexist Statements Commonly Used at the Workplace
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    Chapter 12 Hybrid Dimensionality Reduction Technique for Hyperspectral Images Using Random Projection and Manifold Learning
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    Chapter 13 Clustering Spatial Data via Mixture Models with Dynamic Weights
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    Chapter 14 Temporal Convolutional Network Based Transfer Learning for Structural Health Monitoring of Composites
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    Chapter 15 Dilated Convolutional Recurrent Deep Network with Transfer Learning for Remaining Useful Life Prediction
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    Chapter 16 Generative Modeling for Synthesis of Cellular Imaging Data for Low-Cost Drug Repurposing Application
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    Chapter 17 A Study of Game Payment Data Mining: Predicting High-Value Users for MMORPGs
Attention for Chapter 10: Predictive K-means with Local Models
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1 X user

Citations

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Readers on

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Chapter title
Predictive K-means with Local Models
Chapter number 10
Book title
Trends and Applications in Knowledge Discovery and Data Mining
Published in
arXiv, May 2020
DOI 10.1007/978-3-030-60470-7_10
Book ISBNs
978-3-03-060469-1, 978-3-03-060470-7
Authors

Vincent Lemaire, Oumaima Alaoui Ismaili, Antoine Cornuéjols, Dominique Gay, Lemaire, Vincent, Ismaili, Oumaima Alaoui, Cornuéjols, Antoine, Gay, Dominique

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X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 29%
Professor > Associate Professor 2 29%
Researcher 1 14%
Unknown 2 29%
Readers by discipline Count As %
Psychology 2 29%
Computer Science 1 14%
Arts and Humanities 1 14%
Unknown 3 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 December 2020.
All research outputs
#20,653,779
of 23,248,929 outputs
Outputs from arXiv
#684,212
of 958,529 outputs
Outputs of similar age
#330,010
of 385,609 outputs
Outputs of similar age from arXiv
#22,238
of 33,114 outputs
Altmetric has tracked 23,248,929 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 958,529 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 385,609 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 33,114 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.

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