A high-performance limited-memory admissible and real time search algorithm for networks
A high-performance limited-memory admissible and real time search algorithm for networks
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Artificial intelligence: a new synthesis
Artificial intelligence: a new synthesis
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Data Mining for Scientific and Engineering Applications
Data Mining for Scientific and Engineering Applications
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Scalable Algorithms for Association Mining
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
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
CBW: An Efficient Algorithm for Frequent Itemset Mining
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 3 - Volume 3
Memory issues in frequent itemset mining
Proceedings of the 2004 ACM symposium on Applied computing
An Efficient Technique for Frequent Pattern Mining in Real-Time Business Applications
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 3 - Volume 03
The Real-Time Enterprise
Mining the customer's up-to-moment preferences for e-commerce recommendation
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Finding frequent patterns from databases has been the most researched topic in association-rule mining. Business-Intelligence using data-mining has felt an increased thrust for real-time frequent pattern mining algorithms finding huge demand from numerous realtime business applications like e-commerce, recommender-systems, group-decision-supportsystems, supply-chain-management, to name a few. Last decade has seen development of mind-whelming algorithms, among which, vertical-mining algorithms have been found to be very effective. However, with dense-datasets, the performances of these algorithms significantly degrade. Moreover, these algorithms are not suited to respond to the real-time need. In this paper, we describe BDFS(b)-diff-sets, an algorithm to perform real-time frequent pattern mining using diffsets and using an intelligent staged search technique, by-passing usual breadth-first and depth-first searchtechniques. Empirical evaluations show that our algorithm can make a fair estimation of the probable frequent-patterns reacting to the user-defined time bound and reaches some of the longest frequent patterns much faster than the existing algorithms.