New Frontiers in Mining Complex Patterns

1. Front Matter

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2. Mining Rich (Relational) Datasets

   1. Learning with Configurable Operators and RL-Based Heuristics

      • Fernando Martínez-Plumed, Cèsar Ferri, José Hernández-Orallo, María José Ramírez-Quintana

      Pages 1-16

   2. Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses

      • Ondřej Kuželka, Andrea Szabóová, Filip Železný

      Pages 17-32

3. Mining Complex Patterns from Miscellaneous Data

   1. Mining Complex Event Patterns in Computer Networks

      • Dietmar Seipel, Philipp Neubeck, Stefan Köhler, Martin Atzmueller

      Pages 33-48

   2. Learning in the Presence of Large Fluctuations: A Study of Aggregation and Correlation

      • Eric Paquet, Herna Lydia Viktor, Hongyu Guo

      Pages 49-63

   3. Retracted: Machine Learning as an Objective Approach to Understanding Music

      • Claire Q, Ross D. King

      Pages 64-78

   4. Pair-Based Object-Driven Action Rules

      • Ayman Hajja, Alicja A. Wieczorkowska, Zbigniew W. Ras, Ryszard Gubrynowicz

      Pages 79-93

4. Mining Complex Patterns from Trajectory and Sequence Data

   1. Effectively Grouping Trajectory Streams

      • Gianni Costa, Giuseppe Manco, Elio Masciari

      Pages 94-108

   2. Healthcare Trajectory Mining by Combining Multidimensional Component and Itemsets

      • Elias Egho, Chedy Raïssi, Dino Ienco, Nicolas Jay, Amedeo Napoli, Pascal Poncelet et al.

      Pages 109-123

   3. Graph-Based Approaches to Clustering Network-Constrained Trajectory Data

      • Mohamed Khalil El Mahrsi, Fabrice Rossi

      Pages 124-137

5. Mining Complex Patterns from Graphs and Networks

   1. Finding the Most Descriptive Substructures in Graphs with Discrete and Numeric Labels

      • Michael Davis, Weiru Liu, Paul Miller

      Pages 138-154

   2. Learning in Probabilistic Graphs Exploiting Language-Constrained Patterns

      • Claudio Taranto, Nicola Di Mauro, Floriana Esposito

      Pages 155-169

   3. Improving Robustness and Flexibility of Concept Taxonomy Learning from Text

      • Fabio Leuzzi, Stefano Ferilli, Fulvio Rotella

      Pages 170-184

   4. Discovering Evolution Chains in Dynamic Networks

      • Corrado Loglisci, Michelangelo Ceci, Donato Malerba

      Pages 185-199

   5. Supporting Information Spread in a Social Internetworking Scenario

      • Francesco Buccafurri, Gianluca Lax, Antonino Nocera, Domenico Ursino

      Pages 200-214

   6. Context-Aware Predictions on Business Processes: An Ensemble-Based Solution

      • Francesco Folino, Massimo Guarascio, Luigi Pontieri

      Pages 215-229

   7. Erratum: Machine Learning as an Objective Approach to Understanding Music

      • Claire Q, Ross D. King

      Pages E1-E1

6. Back Matter

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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.

• data mining
• feature selection
• log mining
• probabilistic graphs
• social web

• Dipartimento di Informatica, Università degli Studi di Bari Aldo Moro, Bari, Italy

  Annalisa Appice, Michelangelo Ceci, Corrado Loglisci

• Institute for High Performance Computing and Networks (ICAR), National Research Council (CNR), Rende, Italy

  Giuseppe Manco, Elio Masciari

• Department of Computer Science, University of North Caroline, Charlotte, USA

  Zbigniew W. Ras

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