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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
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New Frontiers in Mining Complex Patterns
Published by
Springer Berlin Heidelberg, March 2013
DOI 10.1007/978-3-642-37382-4
978-3-64-237381-7, 978-3-64-237382-4

Appice, Annalisa, Ceci, Michelangelo, Loglisci, Corrado, Manco, Giuseppe, Masciari, Elio, Ras, Zbigniew W.

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Mendeley readers

The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
Brazil 1 1%
United States 1 1%
Unknown 76 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 41%
Student > Master 13 16%
Unspecified 9 11%
Researcher 7 9%
Student > Doctoral Student 7 9%
Other 11 14%
Readers by discipline Count As %
Computer Science 48 61%
Unspecified 13 16%
Engineering 7 9%
Business, Management and Accounting 4 5%
Social Sciences 2 3%
Other 5 6%