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Advances in Intelligent Data Analysis XII

Overview of attention for book
Cover of 'Advances in Intelligent Data Analysis XII'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Data, Not Dogma: Big Data, Open Data, and the Opportunities Ahead
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    Chapter 2 Computational Techniques for Crop Disease Monitoring in the Developing World
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    Chapter 3 Subjective Interestingness in Exploratory Data Mining
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    Chapter 4 Time Point Estimation of a Single Sample from High Throughput Experiments Based on Time-Resolved Data and Robust Correlation Measures
  6. Altmetric Badge
    Chapter 5 Advances in Intelligent Data Analysis XII
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    Chapter 6 Graph Clustering by Maximizing Statistical Association Measures
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    Chapter 7 Evaluation of Association Rule Quality Measures through Feature Extraction
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    Chapter 8 Towards Comprehensive Concept Description Based on Association Rules
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    Chapter 9 CD-MOA: Change Detection Framework for Massive Online Analysis
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    Chapter 10 Integrating Multiple Studies of Wheat Microarray Data to Identify Treatment-Specific Regulatory Networks
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    Chapter 11 Finding Frequent Patterns in Parallel Point Processes
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    Chapter 12 Behavioral Clustering for Point Processes
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    Chapter 13 Estimating Prediction Certainty in Decision Trees
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    Chapter 14 Interactive Discovery of Interesting Subgroup Sets
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    Chapter 15 Gaussian Mixture Models for Time Series Modelling, Forecasting, and Interpolation
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    Chapter 16 When Does Active Learning Work?
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    Chapter 17 OrderSpan: Mining Closed Partially Ordered Patterns
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    Chapter 18 Learning Multiple Temporal Matching for Time Series Classification
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    Chapter 19 On the importance of nonlinear modeling in computer performance prediction
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    Chapter 20 Diversity-Driven Widening
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    Chapter 21 Towards Indexing of Web3D Signing Avatars
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    Chapter 22 Variational Bayesian PCA versus k -NN on a Very Sparse Reddit Voting Dataset
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    Chapter 23 Analysis of Cluster Structure in Large-Scale English Wikipedia Category Networks
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    Chapter 24 1d-SAX: A Novel Symbolic Representation for Time Series
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    Chapter 25 Learning Models of Activities Involving Interacting Objects
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    Chapter 26 Correcting the Usage of the Hoeffding Inequality in Stream Mining
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    Chapter 27 Exploratory Data Analysis through the Inspection of the Probability Density Function of the Number of Neighbors
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    Chapter 28 The Modelling of Glaucoma Progression through the Use of Cellular Automata
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    Chapter 29 Towards Narrative Ideation via Cross-Context Link Discovery Using Banded Matrices
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    Chapter 30 Gaussian Topographic Co-clustering Model
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    Chapter 31 Preventing Churn in Telecommunications: The Forgotten Network
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    Chapter 32 Computational Properties of Fiction Writing and Collaborative Work
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    Chapter 33 Classifier Evaluation with Missing Negative Class Labels
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    Chapter 34 Dynamic MMHC: A Local Search Algorithm for Dynamic Bayesian Network Structure Learning
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    Chapter 35 Accurate Visual Features for Automatic Tag Correction in Videos
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    Chapter 36 Ontology Database System and Triggers
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    Chapter 37 A Policy Iteration Algorithm for Learning from Preference-Based Feedback
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    Chapter 38 Multiclass Learning from Multiple Uncertain Annotations
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    Chapter 39 Learning Compositional Hierarchies of a Sensorimotor System
Attention for Chapter 19: On the importance of nonlinear modeling in computer performance prediction
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

6 X users
1 Google+ user


1 Dimensions

Readers on

52 Mendeley
1 CiteULike
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Chapter title
On the importance of nonlinear modeling in computer performance prediction
Chapter number 19
Book title
Advances in Intelligent Data Analysis XII
Published in
arXiv, May 2013
DOI 10.1007/978-3-642-41398-8_19
Book ISBNs
978-3-64-241397-1, 978-3-64-241398-8

Joshua Garland, Elizabeth Bradley, Garland, Joshua, Bradley, Elizabeth

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users 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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 2%
United Kingdom 1 2%
Mexico 1 2%
Estonia 1 2%
Greece 1 2%
United States 1 2%
Unknown 46 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 35%
Researcher 7 13%
Student > Bachelor 4 8%
Student > Master 4 8%
Student > Doctoral Student 3 6%
Other 12 23%
Unknown 4 8%
Readers by discipline Count As %
Computer Science 13 25%
Engineering 6 12%
Psychology 5 10%
Physics and Astronomy 4 8%
Agricultural and Biological Sciences 3 6%
Other 13 25%
Unknown 8 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 28 April 2017.
All research outputs
of 22,711,242 outputs
Outputs from arXiv
of 930,878 outputs
Outputs of similar age
of 195,531 outputs
Outputs of similar age from arXiv
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Altmetric has tracked 22,711,242 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 930,878 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done well, scoring higher than 87% of its peers.
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 195,531 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 7,694 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.