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Machine Learning and Data Mining in Pattern Recognition

Overview of attention for book
Cover of 'Machine Learning and Data Mining in Pattern Recognition'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Greedy Graph Edit Distance
  3. Altmetric Badge
    Chapter 2 Learning Heuristics to Reduce the Overestimation of Bipartite Graph Edit Distance Approximation
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    Chapter 3 Seizure Prediction by Graph Mining, Transfer Learning, and Transformation Learning
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    Chapter 4 Local and Global Genetic Fuzzy Pattern Classifiers
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    Chapter 5 IKLTSA: An Incremental Kernel LTSA Method
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    Chapter 6 SentiSAIL: Sentiment Analysis in English, German and Russian
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    Chapter 7 Sentiment Analysis for Government: An Optimized Approach
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    Chapter 8 A Novel Algorithm for the Integration of the Imputation of Missing Values and Clustering
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    Chapter 9 Improving the Algorithm for Mapping of OWL to Relational Database Schema
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    Chapter 10 Robust Principal Component Analysis of Data with Missing Values
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    Chapter 11 Efficient Mining of High-Utility Sequential Rules
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    Chapter 12 MOGACAR: A Method for Filtering Interesting Classification Association Rules
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    Chapter 13 Classifying Grasslands and Cultivated Pastures in the Brazilian Cerrado Using Support Vector Machines, Multilayer Perceptrons and Autoencoders
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    Chapter 14 Hybrid Approach for Inductive Semi Supervised Learning using Label Propagation and Support Vector Machine
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    Chapter 15 Optimizing the Data-Process Relationship for Fast Mining of Frequent Itemsets in MapReduce
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    Chapter 16 Aggregation-Aware Compression of Probabilistic Streaming Time Series
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    Chapter 17 Applying Clustering Analysis to Heterogeneous Data Using Similarity Matrix Fusion (SMF)
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    Chapter 18 On Bicluster Aggregation and its Benefits for Enumerative Solutions
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    Chapter 19 Semi-Supervised Stream Clustering Using Labeled Data Points
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    Chapter 20 Avalanche: A Hierarchical, Divisive Clustering Algorithm
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    Chapter 21 Author Attribution of Email Messages Using Parse-Tree Features
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    Chapter 22 Robust Principal Component Analysis of Data with Missing Values
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    Chapter 23 Offline Writer Identification in Tamil Using Bagged Classification Trees
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    Chapter 24 Data Analysis for Courses Registration
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    Chapter 25 Learning the Relationship Between Corporate Governance and Company Performance Using Data Mining
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    Chapter 26 A Bayesian Approach to Sparse Learning-to-Rank for Search Engine Optimization
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    Chapter 27 Data Driven Geometry for Learning
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    Chapter 28 Mining Educational Data to Predict Students’ Academic Performance
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    Chapter 29 Patient-Specific Modeling of Medical Data
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    Chapter 30 A Bayesian Approach to Sparse Cox Regression in High-Dimentional Survival Analysis
  32. Altmetric Badge
    Chapter 31 Automatic Cell Tracking and Kinetic Feature Description of Cell Paths for Image Mining
Overall attention for this book and its chapters
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Mentioned by

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2 X users

Citations

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7 Dimensions

Readers on

mendeley
50 Mendeley
Summary X Dimensions citations
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Title
Machine Learning and Data Mining in Pattern Recognition
Published by
Lecture notes in computer science, January 2015
DOI 10.1007/978-3-319-21024-7
ISBNs
978-3-31-921023-0, 978-3-31-921024-7
Authors

Petra Perner

Editors

Perner, Petra

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 16%
Student > Ph. D. Student 2 4%
Professor 1 2%
Unknown 39 78%
Readers by discipline Count As %
Computer Science 7 14%
Engineering 4 8%
Business, Management and Accounting 1 2%
Unknown 38 76%
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 08 December 2015.
All research outputs
#18,116,235
of 23,271,751 outputs
Outputs from Lecture notes in computer science
#5,962
of 8,163 outputs
Outputs of similar age
#244,522
of 355,501 outputs
Outputs of similar age from Lecture notes in computer science
#193
of 258 outputs
Altmetric has tracked 23,271,751 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,163 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 25th percentile – i.e., 25% 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 355,501 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 258 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.

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