C4.5: programs for machine learning
C4.5: programs for machine learning
CLIP: concept learning from inference patterns
Artificial Intelligence - Special issue: AI research in Japan
Machine learning techniques to make computers easier to use
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Machine Learning
Machine Learning
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Algorithms for Sequential Patterns in Parallel: Hash Based Approach
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Extension of Graph-Based Induction for General Graph Structured Data
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Tables, Graphs and Logic for Induction
Machine Intelligence 15, Intelligent Agents [St. Catherine's College, Oxford, July 1995]
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
Meta-analysis of Mutagenes Discovery
DS '01 Proceedings of the 4th International Conference on Discovery Science
| Hi-index | 0.00 |
Most of the relations are represented by a graph structure, e.g., chemical bonding, Web browsing record, DNA sequence, Inference pattern (program trace), to name a few. Thus, efficiently finding characteristic substructures in a graph will be a useful technique in many important KDD/ML applications. However, graph pattern matching is a hard problem. We propose a machine learning technique called Graph-Based Induction (GBI) that efficiently extracts typical patterns from graph data in an approximate manner by stepwise pair expansion (pairwise chunking). It can handle general graph structured data, i.e., directed/ undirected, colored/uncolored graphs with/without (self) loop and with colored/uncolored links. We show that its time complexity is almost linear with the size of graph. We, further, show that GBI can effectively be applied to the extraction of typical patterns from chemical compound data from which to generate classification rules, and that GBI also works as a feature construction component for other machine learning tools.