CLIP: concept learning from inference patterns
Artificial Intelligence - Special issue: AI research in Japan
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Representative Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Mining Association Rules for Estimation and Prediction
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
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
Machine learning techniques to make computers easier to use
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
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
Derivation of the Topology Structure from Massive Graph Data
DS '99 Proceedings of the Second International Conference on Discovery Science
Mining for paths in flow graphs
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
| Hi-index | 0.00 |
The Basket Analysis derives frequent itemsets and association rules having support and confidence levels greater than their thresholds from massive transaction data. Though some recent research tries to discover wider classes of knowledge on the regularities contained in the data, the regularities in form of the graph structure has not been explored in the field of the Basket Analysis. The work reported in this paper proposes a new method to mine frequent graph structure appearing in the massive amount of transactions. A specific procedure to preprocess graph structured transactions is introduced to enable the application of the Basket Analysis to extract frequently appearing graph patterns. The basic performance of our proposing approach has been evaluated by a set of graph structured transactions generated by an artificial simulation. Moreover, its practicality has been confirmed through the appliaction to discover popular browsing patterns of clients in WWW URL network.