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Machine Learning and Data Mining in Pattern Recognition

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Cover of 'Machine Learning and Data Mining in Pattern Recognition'

Table of Contents

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    Book Overview
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    Chapter 1 Fusing Dimension Reduction and Classification for Mining Interesting Frequent Patterns in Patients Data
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    Chapter 2 Memory Efficient Frequent Itemset Mining
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    Chapter 3 Fuzzy Networks Model, a Reliable Adoption in Corporations
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    Chapter 4 Detection of Computer-Generated Papers Using One-Class SVM and Cluster Approaches
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    Chapter 5 Understanding Customers and Their Grouping via WiFi Sensing for Business Revenue Forecasting
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    Chapter 6 Evaluation of Hybrid Classification Approaches: Case Studies on Credit Datasets
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    Chapter 7 An Efficient Two-Layer Classification Approach for Hyperspectral Images
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    Chapter 8 Predicting Social Unrest Using GDELT
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    Chapter 9 Ten Years of Relevance Score for Content Based Image Retrieval
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    Chapter 10 When Different Is Wrong: Visual Unsupervised Validation for Web Information Extraction
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    Chapter 11 An Efficient Approximate EMST Algorithm for Color Image Segmentation
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    Chapter 12 Personalized Blended E-learning System Using Knowledge Base Approach Based on Information Processing Speed Cognitive
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    Chapter 13 A Tag2Vec Approach for Questions Tag Suggestion on Community Question Answering Sites
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    Chapter 14 A Crowd Sensing Approach to Video Classification of Traffic Accident Hotspots
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    Chapter 15 Spam Review Detection Using Ensemble Machine Learning
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    Chapter 16 Automatic Keyphrase Extraction Using Recurrent Neural Networks
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    Chapter 17 The Wild Bootstrap Resampling in Regression Imputation Algorithm with a Gaussian Mixture Model
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    Chapter 18 Association Rule Mining in Fuzzy Political Donor Communities
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    Chapter 19 Educational Data Mining: An Application of Regressors in Predicting School Dropout
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    Chapter 20 Reinforcement Learning for Computer Vision and Robot Navigation
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    Chapter 21 A Fast Two-Level Approximate Euclidean Minimum Spanning Tree Algorithm for High-Dimensional Data
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    Chapter 22 Rule Induction Partitioning Estimator
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    Chapter 23 A CNN Based Transfer Learning Model for Automatic Activity Recognition from Accelerometer Sensors
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    Chapter 24 A Mixture of Personalized Experts for Human Affect Estimation
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    Chapter 25 Learning to Rank and Discover for E-Commerce Search
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    Chapter 26 A Hybrid Neural Machine Translation Technique for Translating Low Resource Languages
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    Chapter 27 Prediction of Re-tweeting Activities in Social Networks Based on Event Popularity and User Connectivity
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    Chapter 28 Flow Prediction Versus Flow Simulation Using Machine Learning Algorithms
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    Chapter 29 A Method of Biomedical Knowledge Discovery by Literature Mining Based on SPO Predications: A Case Study of Induced Pluripotent Stem Cells
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    Chapter 30 Constrained Regularized Regression Model Search in Large Sets of Regressors
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    Chapter 31 Large-Scale Targeted Marketing by Supervised PageRank with Seeds
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    Chapter 32 Storytelling with Signal Injection: Focusing Stories with Domain Knowledge
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    Chapter 33 Finding Active Expert Users for Question Routing in Community Question Answering Sites
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    Chapter 34 Parallel Framework for Unsupervised Classification of Seismic Facies
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    Chapter 35 Recognizing Motor Imagery Tasks Using Deep Multi-Layer Perceptrons
Attention for Chapter 2: Memory Efficient Frequent Itemset Mining
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Chapter title
Memory Efficient Frequent Itemset Mining
Chapter number 2
Book title
Machine Learning and Data Mining in Pattern Recognition
Published by
Springer, Cham, July 2018
DOI 10.1007/978-3-319-96133-0_2
Book ISBNs
978-3-31-996132-3, 978-3-31-996133-0
Authors

Nima Shahbazi, Rohollah Soltani, Jarek Gryz, Shahbazi, Nima, Soltani, Rohollah, Gryz, Jarek

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 5%
Canada 1 5%
Australia 1 5%
Brazil 1 5%
Unknown 17 81%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 29%
Student > Bachelor 3 14%
Student > Master 3 14%
Professor > Associate Professor 2 10%
Researcher 2 10%
Other 3 14%
Unknown 2 10%
Readers by discipline Count As %
Computer Science 16 76%
Business, Management and Accounting 2 10%
Arts and Humanities 1 5%
Unknown 2 10%