CLIP: concept learning from inference patterns
Artificial Intelligence - Special issue: AI research in Japan
Machine Learning
Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Basket Analysis for Graph Structured Data
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
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
DS '00 Proceedings of the Third International Conference on Discovery Science
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
A General Framework for Mining Frequent Subgraphs from Labeled Graphs
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
Weighted path as a condensed pattern in a single attributed DAG
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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A machine learning technique called Graph-Based Induction (GBI) efficiently extracts typical patterns from directed graph data by stepwise pair expansion (pairwise chunking). In this paper, we expand the capability of the Graph-Based Induction to handle not only tree structured data but also multi-inputs/outputs nodes and loop structure (including a self-loop) which cannot be treated in the conventional way. The method is verified to work as expected using artificially generated data and we evaluated experimentally the computation time of the implemented program. We, further, show the effectiveness of our approach by applying it to two kinds of the real-world data: World Wide Web browsing data and DNA sequence data.