Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets
CL '00 Proceedings of the First International Conference on Computational Logic
Discovery of Web Communities Based on the Co-Occurence of References
DS '00 Proceedings of the Third International Conference on Discovery Science
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An Efficient Branch-and-bound Algorithm for Finding a Maximum Clique with Computational Experiments
Journal of Global Optimization
Finding Conceptual Document Clusters with Improved Top-N Formal Concept Search
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Formal Concept Analysis: foundations and applications
Formal Concept Analysis: foundations and applications
Efficient mining of association rules based on formal concept analysis
Formal Concept Analysis
Finding Top-N Pseudo Formal Concepts with Core Intents
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
An algorithm for extracting rare concepts with concise intents
ICFCA'10 Proceedings of the 8th international conference on Formal Concept Analysis
Shifting concepts to their associative concepts via bridges
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
| Hi-index | 0.00 |
This paper presents an effective depth-first mining algorithm for finding relatively smaller therefore more implicit groups of Web pages as formal concepts. The algorithm is based on a dynamic ordering method depending on each search node and some search tree expansion rules. Moreover it is designed so as to find top N implicit concepts subject to the size restriction and some space constraints reflecting user’s interests.