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Constructing Decision Trees for Graph-Structured Data by Chunkingless Graph-Based Induction

  • Phu Chien Nguyen
  • Kouzou Ohara
  • Akira Mogi
  • Hiroshi Motoda
  • Takashi Washio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)

Abstract

Chunkingless Graph-Based Induction (Cl-GBI) is a machine learning technique proposed for the purpose of extracting typical patterns from graph-structured data. This method is regarded as an improved version of Graph-Based Induction (GBI) which employs stepwise pair expansion (pairwise chunking) to extract typical patterns from graph-structured data, and can find overlapping patterns that cannot not be found by GBI. In this paper, we propose an algorithm for constructing decision trees for graph-structured data using Cl-GBI. This decision tree construction algorithm, called Decision Tree Chunkingless Graph-Based Induction (DT-ClGBI), can construct decision trees from graph-structured datasets while simultaneously constructing attributes useful for classification using Cl-GBI internally. Since patterns extracted by Cl-GBI are considered as attributes of a graph, and their existence/non-existence are used as attribute values, DT-ClGBI can be conceived as a tree generator equipped with feature construction capability. Experiments were conducted on synthetic and real-world graph-structured datasets showing the effectiveness of the algorithm.

Keywords

Decision Tree Root Node Predictive Accuracy Test Pattern Input Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Phu Chien Nguyen
    • 1
  • Kouzou Ohara
    • 1
  • Akira Mogi
    • 1
  • Hiroshi Motoda
    • 1
  • Takashi Washio
    • 1
  1. 1.Institute of Scientific and Industrial ResearchOsaka UniversityOsakaJapan

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