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New Frontiers in Mining Complex Patterns

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Cover of 'New Frontiers in Mining Complex Patterns'

Table of Contents

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    Book Overview
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    Chapter 1 Learning with Configurable Operators and RL-Based Heuristics
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    Chapter 2 Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses
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    Chapter 3 Mining Complex Event Patterns in Computer Networks
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    Chapter 4 Learning in the Presence of Large Fluctuations: A Study of Aggregation and Correlation
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    Chapter 5 Retracted: Machine Learning as an Objective Approach to Understanding Music
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    Chapter 6 Pair-Based Object-Driven Action Rules
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    Chapter 7 Effectively Grouping Trajectory Streams
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    Chapter 8 Healthcare Trajectory Mining by Combining Multidimensional Component and Itemsets
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    Chapter 9 Graph-Based Approaches to Clustering Network-Constrained Trajectory Data
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    Chapter 10 Finding the Most Descriptive Substructures in Graphs with Discrete and Numeric Labels
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    Chapter 11 Learning in Probabilistic Graphs Exploiting Language-Constrained Patterns
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    Chapter 12 Improving Robustness and Flexibility of Concept Taxonomy Learning from Text
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    Chapter 13 Discovering Evolution Chains in Dynamic Networks
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    Chapter 14 Supporting Information Spread in a Social Internetworking Scenario
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    Chapter 15 Context-Aware Predictions on Business Processes: An Ensemble-Based Solution
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    Chapter 16 Erratum: Machine Learning as an Objective Approach to Understanding Music
Attention for Chapter 7: Effectively Grouping Trajectory Streams
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Chapter title
Effectively Grouping Trajectory Streams
Chapter number 7
Book title
New Frontiers in Mining Complex Patterns
Published by
Springer, Berlin, Heidelberg, September 2012
DOI 10.1007/978-3-642-37382-4_7
Book ISBNs
978-3-64-237381-7, 978-3-64-237382-4
Authors

Gianni Costa, Giuseppe Manco, Elio Masciari

Mendeley readers

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 %
Brazil 1 8%
Unknown 12 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 31%
Researcher 3 23%
Professor 1 8%
Student > Master 1 8%
Other 1 8%
Other 0 0%
Unknown 3 23%
Readers by discipline Count As %
Computer Science 8 62%
Engineering 2 15%
Unknown 3 23%