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Complex Pattern Mining

Overview of attention for book
Cover of 'Complex Pattern Mining'

Table of Contents

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    Book Overview
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    Chapter 1 Efficient Infrequent Pattern Mining Using Negative Itemset Tree
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    Chapter 2 Hierarchical Adversarial Training for Multi-domain Adaptive Sentiment Analysis
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    Chapter 3 Optimizing C-Index via Gradient Boosting in Medical Survival Analysis
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    Chapter 4 Order-Preserving Biclustering Based on FCA and Pattern Structures
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    Chapter 5 A Text-Based Regression Approach to Predict Bug-Fix Time
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    Chapter 6 A Named Entity Recognition Approach for Albanian Using Deep Learning
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    Chapter 7 A Latitudinal Study on the Use of Sequential and Concurrency Patterns in Deviance Mining
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    Chapter 8 Efficient Declarative-Based Process Mining Using an Enhanced Framework
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    Chapter 9 Exploiting Pattern Set Dissimilarity for Detecting Changes in Communication Networks
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    Chapter 10 Classification and Clustering of Emotive Microblogs in Albanian: Two User-Oriented Tasks
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    Chapter 11 Dealing with Class Imbalance in Android Malware Detection by Cascading Clustering and Classification
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    Chapter 12 Applying Analytics to Artist Provided Text to Model Prices of Fine Art
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    Chapter 13 Approximate Query Answering over Incomplete Data
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    Chapter 14 A Machine Learning Approach for Walker Identification Using Smartphone Sensors
Attention for Chapter 14: A Machine Learning Approach for Walker Identification Using Smartphone Sensors
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mendeley
12 Mendeley
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Chapter title
A Machine Learning Approach for Walker Identification Using Smartphone Sensors
Chapter number 14
Book title
Complex Pattern Mining
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-36617-9_14
Book ISBNs
978-3-03-036616-2, 978-3-03-036617-9
Authors

Antonio Angrisano, Pasquale Ardimento, Mario Luca Bernardi, Marta Cimitile, Salvatore Gaglione, Angrisano, Antonio, Ardimento, Pasquale, Bernardi, Mario Luca, Cimitile, Marta, Gaglione, Salvatore

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 50%
Student > Ph. D. Student 1 8%
Unknown 5 42%
Readers by discipline Count As %
Computer Science 3 25%
Earth and Planetary Sciences 2 17%
Nursing and Health Professions 1 8%
Engineering 1 8%
Unknown 5 42%

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