Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Making use of the most expressive jumping emerging patterns for classification
Knowledge and Information Systems
ECML '93 Proceedings of the European Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
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
On computing, storing and querying frequent patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining top-K covering rule groups for gene expression data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
CFI-Stream: mining closed frequent itemsets in data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Catch the moment: maintaining closed frequent itemsets over a data stream sliding window
Knowledge and Information Systems
On Mining Instance-Centric Classification Rules
IEEE Transactions on Knowledge and Data Engineering
Lazy Associative Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Mining statistically important equivalence classes and delta-discriminative emerging patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient mining of frequent sequence generators
Proceedings of the 17th international conference on World Wide Web
Direct Discriminative Pattern Mining for Effective Classification
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Minimum description length principle: generators are preferable to closed patterns
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
An incremental algorithm for mining generators representation
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Direct mining of discriminative patterns for classifying uncertain data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Key roles of closed sets and minimal generators in concise representations of frequent patterns
Intelligent Data Analysis
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Mining generator patterns has raised great research interest in recent years. The main purpose of mining itemset generators is that they can form equivalence classes together with closed itemsets, and can be used to generate simple classification rules according to the MDL principle. In this paper, we devise an efficient algorithm called StreamGen to mine frequent itemset generators over a stream sliding window. We adopt a novel enumeration tree structure to help keep the information of mined generators and the border between generators and non-generators, and propose some optimization techniques to speed up the mining process. We further extend the algorithm to directly mine a set of high quality classification rules over stream sliding windows while keeping high performance. The extensive performance study shows that our algorithm outperforms other state-of-the-art algorithms which perform similar tasks in terms of both runtime and memory usage efficiency, and has high utility in terms of classification.