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
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
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
Mining Molecular Fragments: Finding Relevant Substructures of Molecules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
An Efficient Algorithm for Discovering Frequent Subgraphs
IEEE Transactions on Knowledge and Data Engineering
GREW-A Scalable Frequent Subgraph Discovery Algorithm
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
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
Graph-Based Procedural Abstraction
Proceedings of the International Symposium on Code Generation and Optimization
Pruning Strategies Based on the Upper Bound of Information Gain for Discriminative Subgraph Mining
Knowledge Acquisition: Approaches, Algorithms and Applications
Pattern discovery from graph-structured data: a data mining perspective
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
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
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
What can we do with graph-structured data? – a data mining perspective
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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Graph-Based Induction (GBI) is a machine learning technique developed for the purpose of extracting typical patterns from graph-structured data by stepwise pair expansion (pair-wise chunking). GBI is very efficient because of its greedy search strategy, however, it suffers from the problem of overlapping subgraphs. As a result, some of typical patterns cannot be discovered by GBI though a beam search has been incorporated in an improved version of GBI called Beam-wise GBI (B-GBI). In this paper, improvement is made on the search capability by using a new search strategy, where frequent pairs are never chunked but used as pseudo nodes in the subsequent steps, thus allowing extraction of overlapping subgraphs. This new algorithm, called Cl-GBI (Chunkingless GBI), was tested against two datasets, the promoter dataset from UCI repository and the hepatitis dataset provided by Chiba University, and shown successful in extracting more typical patterns than B-GBI.