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Mining Complex Data

ECML/PKDD 2007 Third International Workshop, MDC 2007, Warsaw, Poland, September 17-21, 2007, Revised Selected Papers

  • Conference proceedings
  • © 2008

Overview

Editors:
  1. Zbigniew W. Raś
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  2. Shusaku Tsumoto
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  3. Djamel Zighed
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 4944)

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

Included in the following conference series:

Conference proceedings info: MCD 2007.

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Table of contents (20 papers)

  1. Front Matter

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  2. Session A1

    1. Using Text Mining and Link Analysis for Software Mining

      • Miha Grcar, Marko Grobelnik, Dunja Mladenic
      Pages 1-12

    2. Generalization-Based Similarity for Conceptual Clustering

      • S. Ferilli, T. M. A. Basile, N. Di Mauro, M. Biba, F. Esposito
      Pages 13-26

    3. Trajectory Analysis of Laboratory Tests as Medical Complex Data Mining

      • Shoji Hirano, Shusaku Tsumoto
      Pages 27-41

  3. Session A2

    1. Conceptual Clustering Applied to Ontologies

      • Floriana Esposito, Nicola Fanizzi, Claudia d’Amato
      Pages 42-56

    2. Feature Selection: Near Set Approach

      • James F. Peters, Sheela Ramanna
      Pages 57-71

    3. Evaluating Accuracies of a Trading Rule Mining Method Based on Temporal Pattern Extraction

      • Hidenao Abe, Satoru Hirabayashi, Miho Ohsaki, Takahira Yamaguchi
      Pages 72-81

  4. Session A3

    1. Discovering Word Meanings Based on Frequent Termsets

      • Henryk Rybinski, Marzena Kryszkiewicz, Grzegorz Protaziuk, Aleksandra Kontkiewicz, Katarzyna Marcinkowska, Alexandre Delteil
      Pages 82-92

    2. Quality of Musical Instrument Sound Identification for Various Levels of Accompanying Sounds

      • Alicja Wieczorkowska, Elżbieta Kolczyńska
      Pages 93-103

    3. Discriminant Feature Analysis for Music Timbre Recognition and Automatic Indexing

      • Xin Zhang, Zbigniew W. Raś, Agnieszka Dardzińska
      Pages 104-115

  5. Session A4

    1. Contextual Adaptive Clustering of Web and Text Documents with Personalization

      • Krzysztof Ciesielski, Mieczysław A. Kłopotek, Sławomir T. Wierzchoń
      Pages 116-130

    2. Improving Boosting by Exploiting Former Assumptions

      • Emna Bahri, Nicolas Nicoloyannis, Mondher Maddouri
      Pages 131-142

    3. Discovery of Frequent Graph Patterns that Consist of the Vertices with the Complex Structures

      • Tsubasa Yamamoto, Tomonobu Ozaki, Takenao Ohkawa
      Pages 143-156

  6. Session B1

    1. Finding Composite Episodes

      • Ronnie Bathoorn, Arno Siebes
      Pages 157-168

    2. Ordinal Classification with Decision Rules

      • Krzysztof Dembczyński, Wojciech Kotłowski, Roman Słowiński
      Pages 169-181

    3. Data Mining of Multi-categorized Data

      • Akinori Abe, Norihiro Hagita, Michiko Furutani, Yoshiyuki Furutani, Rumiko Matsuoka
      Pages 182-195

    4. ARAS: Action Rules Discovery Based on Agglomerative Strategy

      • Zbigniew W. Raś, Elżbieta Wyrzykowska, Hanna Wasyluk
      Pages 196-208

  7. Session B2

    1. Learning to Order: A Relational Approach

      • Donato Malerba, Michelangelo Ceci
      Pages 209-223

    2. Using Semantic Distance in a Content-Based Heterogeneous Information Retrieval System

      • Ahmad El Sayed, Hakim Hacid, Djamel Zighed
      Pages 224-237

    3. Using Secondary Knowledge to Support Decision Tree Classification of Retrospective Clinical Data

      • Dympna O’Sullivan, William Elazmeh, Szymon Wilk, Ken Farion, Stan Matwin, Wojtek Michalowski et al.
      Pages 238-251
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About this book

This book constitutes the refereed proceedings of the Third International Workshop on Mining Complex Data, MCD 2007, held in Warsaw, Poland, in September 2007, co-located with ECML and PKDD 2007. The 20 revised full papers presented were carefully reviewed and selected; they present original results on knowledge discovery from complex data. In contrast to the typical tabular data, complex data can consist of heterogenous data types, can come from different sources, or live in high dimensional spaces. All these specificities call for new data mining strategies.

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