Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Applying MDL to learn best model granularity
Artificial Intelligence
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
The Closed Keys Base of Frequent Itemsets
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Dataless Transitions Between Concise Representations of Frequent Patterns
Journal of Intelligent Information Systems
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
Carpenter: finding closed patterns in long biological datasets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
Mining statistically important equivalence classes and delta-discriminative emerging patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of frequent sequence generators
Proceedings of the 17th international conference on World Wide Web
Mining past-time temporal rules from execution traces
WODA '08 Proceedings of the 2008 international workshop on dynamic analysis: held in conjunction with the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2008)
FOGGER: an algorithm for graph generator discovery
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Adaptive XML Tree Classification on Evolving Data Streams
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Efficient itemset generator discovery over a stream sliding window
Proceedings of the 18th ACM conference on Information and knowledge management
About the lossless reduction of the minimal generator family of a context
ICFCA'07 Proceedings of the 5th international conference on Formal concept analysis
Feature construction based on closedness properties is not that simple
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Feature construction and δ-free sets in 0/1 samples
DS'06 Proceedings of the 9th international conference on Discovery Science
CPCQ: Contrast pattern based clustering quality index for categorical data
Pattern Recognition
Efficiently finding the best parameter for the emerging pattern-based classifier PCL
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Revisiting numerical pattern mining with formal concept analysis
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
MSGPs: a novel algorithm for mining sequential generator patterns
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
Mining succinct predicated bug signatures
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
International Journal of Intelligent Information and Database Systems
Key roles of closed sets and minimal generators in concise representations of frequent patterns
Intelligent Data Analysis
Hi-index | 0.00 |
The generators and the unique closed pattern of an equivalence class of itemsets share a common set of transactions. The generators are the minimal ones among the equivalent itemsets, while the closed pattern is the maximum one. As a generator is usually smaller than the closed pattern in cardinality, by the Minimum Description Length Principle, the generator is preferable to the closed pattern in inductive inference and classification. To efficiently discover frequent generators from a large dataset, we develop a depth-first algorithm called Gr-growth. The idea is novel in contrast to traditional breadth-first bottom-up generator-mining algorithms. Our extensive performance study shows that Gr-growth is significantly faster (an order or even two orders of magnitudes when the support thresholds are low) than the existing generator mining algorithms. It can be also faster than the state-of-the-art frequent closed itemset mining algorithms such as FPclose and CLOSET+.