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Advances in Intelligent Data Analysis XIII

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

Table of Contents

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    Book Overview
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    Chapter 1 Malware Phylogenetics Based on the Multiview Graphical Lasso
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    Chapter 2 Modeling Stationary Data by a Class of Generalized Ornstein-Uhlenbeck Processes: The Gaussian Case
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    Chapter 3 An Approach to Controlling the Runtime for Search Based Modularisation of Sequential Source Code Check-ins
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    Chapter 4 Simple Pattern Spectrum Estimation for Fast Pattern Filtering with CoCoNAD
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    Chapter 5 Advances in Intelligent Data Analysis XIII
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    Chapter 6 Comparing Pre-defined Software Engineering Metrics with Free-Text for the Prediction of Code ‘Ripples’
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    Chapter 7 ApiNATOMY: Towards Multiscale Views of Human Anatomy
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    Chapter 8 Granularity of Co-evolution Patterns in Dynamic Attributed Graphs
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    Chapter 9 Multi-user Diverse Recommendations through Greedy Vertex-Angle Maximization
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    Chapter 10 ERMiner: Sequential Rule Mining Using Equivalence Classes
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    Chapter 11 Mining Longitudinal Epidemiological Data to Understand a Reversible Disorder
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    Chapter 12 The BioKET Biodiversity Data Warehouse: Data and Knowledge Integration and Extraction
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    Chapter 13 Advances in Intelligent Data Analysis XIII
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    Chapter 14 Modeling Daily Profiles of Solar Global Radiation Using Statistical and Data Mining Techniques
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    Chapter 15 Identification of Bilingual Segments for Translation Generation
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    Chapter 16 Advances in Intelligent Data Analysis XIII
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    Chapter 17 Fast Simultaneous Clustering and Feature Selection for Binary Data
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    Chapter 18 Instant Exceptional Model Mining Using Weighted Controlled Pattern Sampling
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    Chapter 19 Resampling Approaches to Improve News Importance Prediction
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    Chapter 20 An Incremental Probabilistic Model to Predict Bus Bunching in Real-Time
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    Chapter 21 Mining Representative Frequent Patterns in a Hierarchy of Contexts
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    Chapter 22 A Deep Interpretation of Classifier Chains
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    Chapter 23 A Nonparametric Mixture Model for Personalizing Web Search
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    Chapter 24 Widened KRIMP: Better Performance through Diverse Parallelism
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    Chapter 25 Finding the Intrinsic Patterns in a Collection of Time Series
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    Chapter 26 A Spatio-temporal Bayesian Network Approach for Revealing Functional Ecological Networks in Fisheries
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    Chapter 27 Extracting Predictive Models from Marked-Up Free-Text Documents at the Royal Botanic Gardens, Kew, London
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    Chapter 28 Detecting Localised Anomalous Behaviour in a Computer Network
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    Chapter 29 Indirect Estimation of Shortest Path Distributions with Small-World Experiments
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    Chapter 30 Parametric Nonlinear Regression Models for Dike Monitoring Systems
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    Chapter 31 Exploiting novel properties of space-filling curves for data analysis
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    Chapter 32 RealKrimp — Finding Hyperintervals that Compress with MDL for Real-Valued Data
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    Chapter 33 Real-Time Adaptive Residual Calculation for Detecting Trend Deviations in Systems with Natural Variability
Overall attention for this book and its chapters
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
13 Mendeley
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Title
Advances in Intelligent Data Analysis XIII
Published by
Lecture notes in computer science, January 2014
DOI 10.1007/978-3-319-12571-8
ISBNs
978-3-31-912570-1, 978-3-31-912571-8
Authors

Jaber, Mohammad, Papapetrou, Panagiotis, Helmer, Sven, Wood, Peter T., Hendrik Blockeel, Matthijs van Leeuwen, Veronica Vinciotti

Editors

Hendrik Blockeel, Matthijs van Leeuwen, Veronica Vinciotti

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 8%
Unknown 12 92%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 31%
Student > Ph. D. Student 3 23%
Student > Master 2 15%
Lecturer 1 8%
Student > Doctoral Student 1 8%
Other 1 8%
Unknown 1 8%
Readers by discipline Count As %
Computer Science 9 69%
Agricultural and Biological Sciences 2 15%
Engineering 1 8%
Unknown 1 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 26 February 2017.
All research outputs
#3,794,766
of 9,116,047 outputs
Outputs from Lecture notes in computer science
#2,704
of 6,824 outputs
Outputs of similar age
#66,976
of 204,178 outputs
Outputs of similar age from Lecture notes in computer science
#48
of 126 outputs
Altmetric has tracked 9,116,047 research outputs across all sources so far. This one has received more attention than most of these and is in the 57th percentile.
So far Altmetric has tracked 6,824 research outputs from this source. They receive a mean Attention Score of 4.1. This one has gotten more attention than average, scoring higher than 58% 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 204,178 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 126 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.