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
Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Intelligent Systems
Machine Learning
Machine Learning
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
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
The levelwise version space algorithm and its application to molecular fragment finding
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Constructing a Decision Tree for Graph-Structured Data and its Applications
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Pruning Strategies Based on the Upper Bound of Information Gain for Discriminative Subgraph Mining
Knowledge Acquisition: Approaches, Algorithms and Applications
Classifier construction by graph-based induction for graph-structured data
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Data analysis and bioinformatics
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Constructing decision trees for graph-structured data by chunkingless graph-based induction
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Graph clustering based on structural similarity of fragments
Proceedings of the 2005 international conference on Federation over the Web
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
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
Constructing a Decision Tree for Graph-Structured Data and its Applications
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
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A machine learning technique called Graph-Based Induction (GBI) extracts typical patterns from graph data by stepwise pair expansion (pairwise chunking). Because of its greedy search strategy, it is very efficient but suffers from incompleteness of search. Improvement is made on its search capability without imposing much computational complexity by 1) incorporating a beam search, 2) using a different evaluation function to extract patterns that are more discriminatory than those simply occurring frequently, and 3) adopting canonical labeling to enumerate identical patterns accurately. This new algorithm, now called Beam-wise GBI, B-GBI for short, was tested against a small DNA dataset from UCI repository and shown successful in extracting discriminatory substructures.