Ebook: New Frontiers in Mining Complex Patterns: First International Workshop, NFMCP 2012, Held in Conjunction with ECML/PKDD 2012, Bristol, UK, September 24, 2012, Rivesed Selected Papers

This book constitutes the thoroughly refereed conference proceedings of the First International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2012, held in conjunction with ECML/PKDD 2012, in Bristol, UK, in September 2012.
The 15 revised full papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on mining rich (relational) datasets, mining complex patterns from miscellaneous data, mining complex patterns from trajectory and sequence data, and mining complex patterns from graphs and networks.

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This book constitutes the thoroughly refereed conference proceedings of the First International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2012, held in conjunction with ECML/PKDD 2012, in Bristol, UK, in September 2012.
The 15 revised full papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on mining rich (relational) datasets, mining complex patterns from miscellaneous data, mining complex patterns from trajectory and sequence data, and mining complex patterns from graphs and networks.

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This book constitutes the thoroughly refereed conference proceedings of the First International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2012, held in conjunction with ECML/PKDD 2012, in Bristol, UK, in September 2012.
The 15 revised full papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on mining rich (relational) datasets, mining complex patterns from miscellaneous data, mining complex patterns from trajectory and sequence data, and mining complex patterns from graphs and networks.
Content:
Front Matter....Pages -
Learning with Configurable Operators and RL-Based Heuristics....Pages 1-16
Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses....Pages 17-32
Mining Complex Event Patterns in Computer Networks....Pages 33-48
Learning in the Presence of Large Fluctuations: A Study of Aggregation and Correlation....Pages 49-63
Machine Learning as an Objective Approach to Understanding Music....Pages 64-78
Pair-Based Object-Driven Action Rules....Pages 79-93
Effectively Grouping Trajectory Streams....Pages 94-108
Healthcare Trajectory Mining by Combining Multidimensional Component and Itemsets....Pages 109-123
Graph-Based Approaches to Clustering Network-Constrained Trajectory Data....Pages 124-137
Finding the Most Descriptive Substructures in Graphs with Discrete and Numeric Labels....Pages 138-154
Learning in Probabilistic Graphs Exploiting Language-Constrained Patterns....Pages 155-169
Improving Robustness and Flexibility of Concept Taxonomy Learning from Text....Pages 170-184
Discovering Evolution Chains in Dynamic Networks....Pages 185-199
Supporting Information Spread in a Social Internetworking Scenario....Pages 200-214
Context-Aware Predictions on Business Processes: An Ensemble-Based Solution....Pages 215-229
Back Matter....Pages -

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This book constitutes the thoroughly refereed conference proceedings of the First International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2012, held in conjunction with ECML/PKDD 2012, in Bristol, UK, in September 2012.
The 15 revised full papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on mining rich (relational) datasets, mining complex patterns from miscellaneous data, mining complex patterns from trajectory and sequence data, and mining complex patterns from graphs and networks.
Content:
Front Matter....Pages -
Learning with Configurable Operators and RL-Based Heuristics....Pages 1-16
Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses....Pages 17-32
Mining Complex Event Patterns in Computer Networks....Pages 33-48
Learning in the Presence of Large Fluctuations: A Study of Aggregation and Correlation....Pages 49-63
Machine Learning as an Objective Approach to Understanding Music....Pages 64-78
Pair-Based Object-Driven Action Rules....Pages 79-93
Effectively Grouping Trajectory Streams....Pages 94-108
Healthcare Trajectory Mining by Combining Multidimensional Component and Itemsets....Pages 109-123
Graph-Based Approaches to Clustering Network-Constrained Trajectory Data....Pages 124-137
Finding the Most Descriptive Substructures in Graphs with Discrete and Numeric Labels....Pages 138-154
Learning in Probabilistic Graphs Exploiting Language-Constrained Patterns....Pages 155-169
Improving Robustness and Flexibility of Concept Taxonomy Learning from Text....Pages 170-184
Discovering Evolution Chains in Dynamic Networks....Pages 185-199
Supporting Information Spread in a Social Internetworking Scenario....Pages 200-214
Context-Aware Predictions on Business Processes: An Ensemble-Based Solution....Pages 215-229
Back Matter....Pages -
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