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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 8: Efficient Declarative-Based Process Mining Using an Enhanced Framework
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Chapter title
Efficient Declarative-Based Process Mining Using an Enhanced Framework
Chapter number 8
Book title
Complex Pattern Mining
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-36617-9_8
Book ISBNs
978-3-03-036616-2, 978-3-03-036617-9
Authors

Stefano Ferilli, Sergio Angelastro, Ferilli, Stefano, Angelastro, Sergio

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 17%
Lecturer 1 17%
Unknown 4 67%
Readers by discipline Count As %
Computer Science 1 17%
Unknown 5 83%

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