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
Machine Learning
Mining Patterns from Structured Data by Beam-Wise Graph-Based Induction
DS '02 Proceedings of the 5th International Conference on Discovery Science
Constructing a Decision Tree for Graph-Structured Data and its Applications
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
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
Pruning Strategies Based on the Upper Bound of Information Gain for Discriminative Subgraph Mining
Knowledge Acquisition: Approaches, Algorithms and Applications
Extracting discriminative patterns from graph structured data using constrained search
PKAW'06 Proceedings of the 9th Pacific Rim Knowledge Acquisition international conference on Advances in Knowledge Acquisition and Management
| Hi-index | 0.00 |
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.