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
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An Efficient Algorithm for Discovering Frequent Subgraphs
IEEE Transactions on Knowledge and Data Engineering
Constructing a Decision Tree for Graph-Structured Data and its Applications
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
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
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
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A graph mining method, Chunkingless Graph-Based Induction (Cl-GBI), finds typical patterns appearing in graph-structured data by the operation called chunkingless pairwise expansion, or pseudo-chunking which generates pseudo-nodes from selected pairs of nodes in the data. Cl-GBI enables to extract overlapping subgraphs, but it requires more time and space complexities than the older version GBI that employs real chunking. Thus, it happens that Cl-GBI cannot extract patterns that need be large enough to describe characteristics of data within a limited time and given computational resources. In such a case, extracted patterns maynot be so interesting for domain experts. To mine more discriminative patterns which cannot be extracted by the current Cl-GBI, we introduce a search algorithm in which patterns to be searched are guided by domain knowledge or interests of domain experts. We further experimentally show that the proposed method can efficiently extract more discriminative patterns using a real world dataset.