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

  • Annalisa Appice
  • Michelangelo Ceci
  • Corrado Loglisci
  • Giuseppe Manco
  • Elio Masciari
  • Zbigniew W. Ras
Conference proceedings NFMCP 2012

DOI: 10.1007/978-3-642-37382-4

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7765)

Table of contents (16 papers)

  1. Front Matter
  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. 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. Learning in Probabilistic Graphs Exploiting Language-Constrained Patterns
      Claudio Taranto, Nicola Di Mauro, Floriana Esposito
      Pages 155-169
    2. Improving Robustness and Flexibility of Concept Taxonomy Learning from Text
      Fabio Leuzzi, Stefano Ferilli, Fulvio Rotella
      Pages 170-184
    3. Discovering Evolution Chains in Dynamic Networks
      Corrado Loglisci, Michelangelo Ceci, Donato Malerba
      Pages 185-199
    4. Supporting Information Spread in a Social Internetworking Scenario
      Francesco Buccafurri, Gianluca Lax, Antonino Nocera, Domenico Ursino
      Pages 200-214
    5. Context-Aware Predictions on Business Processes: An Ensemble-Based Solution
      Francesco Folino, Massimo Guarascio, Luigi Pontieri
      Pages 215-229
  6. Back Matter

About these proceedings


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

Editors and affiliations

  • Annalisa Appice
    • 1
  • Michelangelo Ceci
    • 1
  • Corrado Loglisci
    • 1
  • Giuseppe Manco
    • 2
  • Elio Masciari
    • 2
  • Zbigniew W. Ras
    • 3
  1. 1.Dipartimento di InformaticaUniversità degli Studi di Bari Aldo MoroBariItaly
  2. 2.Institute for High Performance Computing and Networks (ICAR)National Research Council (CNR)RendeItaly
  3. 3.Department of Computer ScienceUniversity of North CarolineCharlotteUSA

Bibliographic information

  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-37381-7
  • Online ISBN 978-3-642-37382-4
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349