Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
WAR: Weighted Association Rules for Item Intensities
Knowledge and Information Systems
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
Information Sciences: an International Journal
CP-tree: a tree structure for single-pass frequent pattern mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Single-pass incremental and interactive mining for weighted frequent patterns
Expert Systems with Applications: An International Journal
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By considering different weights of the items, weighted frequent pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery. However, existing algorithms cannot be applied for incremental and interactive WFP mining because they are based on a static database and require multiple database scans. In this paper, we present a novel tree structure ${\rm IWFPT}_{\textrm{\scriptsize{WA}}}$ (Incremental WFP tree based on weight ascending order) and an algorithm ${\rm IWFP}_{\textrm{\scriptsize{WA}}}$ for incremental and interactive WFP mining using a single database scan. Extensive performance analyses show that our tree structure and algorithm are efficient for incremental and interactive WFP mining.