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New Frontiers in Mining Complex Patterns

Second International Workshop, NFMCP 2013, Held in Conjunction with ECML-PKDD 2013, Prague, Czech Republic, September 27, 2013, Revised Selected Papers

  • Conference proceedings
  • © 2014

Overview

Editors:
  1. Annalisa Appice

    1. Università degli Studi di Bari Aldo Moro, Bari, Italy

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  2. Michelangelo Ceci

    1. Università degli Studi di Bari Aldo Moro, Bari, Italy

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  3. Corrado Loglisci

    1. Università degli Studi di Bari Aldo Moro, Bari, Italy

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  4. Giuseppe Manco

    1. ICAR, CNR, Rende, Italy

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  5. Elio Masciari

    1. Rende, Italy

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  6. Zbigniew W. Ras

    1. Department of Computer Science, University of North Carolina, Charlotte, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 8399)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: NFMCP 2013.

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About this book

This book constitutes the thoroughly refereed post-conference proceedings of the Second International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2013, held in conjunction with ECML/PKDD 2013 in Prague, Czech Republic, in September 2013. The 16 revised full papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on data streams and time series analysis, classification, clustering and pattern discovery, graphs, networks and relational data, machine learning and music data.

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Keywords

Table of contents (16 papers)

  1. Front Matter

    Pages I-XII
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  2. Data Streams and Time Series Analysis

    1. Front Matter

      Pages 1-1
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    2. Parameter Estimation and Pattern Validation in Flock Mining

      • Rebecca Ong, Mirco Nanni, Chiara Renso, Monica Wachowicz, Dino Pedreschi
      Pages 3-17

    3. Feature Extraction over Multiple Representations for Time Series Classification

      • Dominique Gay, Romain Guigourès, Marc Boullé, Fabrice Clérot
      Pages 18-34

    4. A Classification Based Scoring Function for Continuous Time Bayesian Network Classifiers

      • Daniele Codecasa, Fabio Stella
      Pages 35-50

    5. Trajectory Data Pattern Mining

      • Elio Masciari, Gao Shi, Carlo Zaniolo
      Pages 51-66

    6. Process Mining to Forecast the Future of Running Cases

      • Sonja Pravilovic, Annalisa Appice, Donato Malerba
      Pages 67-81

  3. Classification, Clustering and Pattern Discovery

    1. Front Matter

      Pages 83-83
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    2. A Hybrid Distance-Based Method and Support Vector Machines for Emotional Speech Detection

      • Vladimer Kobayashi
      Pages 85-99

    3. Methods for the Efficient Discovery of Large Item-Indexable Sequential Patterns

      • Rui Henriques, Cláudia Antunes, Sara C. Madeira
      Pages 100-116

    4. Mining Frequent Partite Episodes with Partwise Constraints

      • Takashi Katoh, Shin-ichiro Tago, Tatsuya Asai, Hiroaki Morikawa, Junichi Shigezumi, Hiroya Inakoshi
      Pages 117-131

    5. Structure Determination and Estimation of Hierarchical Archimedean Copulas Based on Kendall Correlation Matrix

      • Jan Górecki, Martin Holeňa
      Pages 132-147

    6. ReliefF for Hierarchical Multi-label Classification

      • Ivica Slavkov, Jana Karcheska, Dragi Kocev, Slobodan Kalajdziski, Sašo Džeroski
      Pages 148-161

    7. The Use of the Label Hierarchy in Hierarchical Multi-label Classification Improves Performance

      • Jurica Levatić, Dragi Kocev, Sašo Džeroski
      Pages 162-177

  4. Graphs, Networks and Relational Data

    1. Front Matter

      Pages 179-179
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    2. Agwan: A Generative Model for Labelled, Weighted Graphs

      • Michael Davis, Weiru Liu, Paul Miller, Ruth F. Hunter, Frank Kee
      Pages 181-200

    3. Thresholding of Semantic Similarity Networks Using a Spectral Graph-Based Technique

      • Pietro Hiram Guzzi, Pierangelo Veltri, Mario Cannataro
      Pages 201-213

    4. A Relational Unsupervised Approach to Author Identification

      • Fabio Leuzzi, Stefano Ferilli, Fulvio Rotella
      Pages 214-228

  5. Machine Learning and Music Data

    1. Front Matter

      Pages 229-229
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    2. From Personalized to Hierarchically Structured Classifiers for Retrieving Music by Mood

      • Amanda Cohen Mostafavi, Zbigniew W. Raś, Alicja A. Wieczorkowska
      Pages 231-245
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Other volumes

  1. New Frontiers in Mining Complex Patterns

Editors and Affiliations

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

    Annalisa Appice, Michelangelo Ceci, Corrado Loglisci

  • ICAR, CNR, Rende, Italy

    Giuseppe Manco

  • Rende, Italy

    Elio Masciari

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

    Zbigniew W. Ras

Bibliographic Information

  • Book Title: New Frontiers in Mining Complex Patterns

  • Book Subtitle: Second International Workshop, NFMCP 2013, Held in Conjunction with ECML-PKDD 2013, Prague, Czech Republic, September 27, 2013, Revised Selected Papers

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

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-319-08407-7

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer International Publishing Switzerland 2014

  • Softcover ISBN: 978-3-319-08406-0Published: 22 July 2014

  • eBook ISBN: 978-3-319-08407-7Published: 05 July 2014

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XII, 261

  • Number of Illustrations: 80 b/w illustrations

  • Topics: Data Mining and Knowledge Discovery, Database Management, Information Storage and Retrieval, Artificial Intelligence

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