C4.5: programs for machine learning
C4.5: programs for machine learning
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
CLIP: concept learning from inference patterns
Artificial Intelligence - Special issue: AI research in Japan
Machine Learning
Knowledge Discovery from Structured Data by Beam-Wise Graph-Based Induction
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Mining Patterns from Structured Data by Beam-Wise Graph-Based Induction
DS '02 Proceedings of the 5th International Conference on Discovery Science
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
Analysis of hepatitis dataset by decision tree based on graph-based induction
JSAI'03/JSAI04 Proceedings of the 2003 and 2004 international conference on New frontiers in artificial intelligence
Extracting diagnostic knowledge from hepatitis dataset by decision tree graph-based induction
AM'03 Proceedings of the Second international conference on Active Mining
Cl-GBI: a novel approach for extracting typical patterns from graph-structured data
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A Dichotomic Search Algorithm for Mining and Learning in Domain-Specific Logics
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Constructing a Decision Tree for Graph-Structured Data and its Applications
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
NODAR: mining globally distributed substructures from a single labeled graph
Journal of Intelligent Information Systems
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A machine learning technique called Graph-Based Induction (GBI) efficiently extracts typical patterns from graph-structured data by stepwise pair expansion (pairwise chunking). It is very efficient because of its greedy search. Meanwhile, a decision tree is an effective means of data classification from which rules that are easy to understand can be obtained. However, a decision tree could not be produced for the data which is not explicitly expressed with attribute-value pairs. In this paper, we proposes a method of constructing a classifier (decision tree) for graph-structured data by GBI. In our approach attributes, namely substructures useful for classification task, are constructed by GBI on the fly while constructing a decision tree. We call this technique Decision Tree - Graph-Based Induction (DT-GBI). DT-GBI was tested against a DNA dataset from UCI repository. Since DNA data is a sequence of symbols, representing each sequence by attribute-value pairs by simply assigning these symbols to the values of ordered attributes does not make sense. The sequences were transformed into graph-structured data and the attributes (substructures) were extracted by GBI to construct a decision tree. Effect of adjusting the number of times to run GBI at each node of a decision tree is evaluated with respect to the predictive accuracy. The results indicate the effectiveness of DT-GBI for constructing a classifier for graph-structured data.