Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
An Adaptive Algorithm for Incremental Mining of Association Rules
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Catch the moment: maintaining closed frequent itemsets over a data stream sliding window
Knowledge and Information Systems
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
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
Kernel-Tree: mining frequent patterns in a data stream based on forecast support
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Since the introduction of FP-growth using FP-tree there has been a lot of research into extending its usage to data stream or incremental mining. Most incremental mining adapts the Apriori algorithm. However, we believe that using a tree based approach would increase performance as compared to the candidate generation and testing mechanism used in Apriori. Despite this FP-tree still requires two scans through a dataset. In this paper we present a novel tree structure called Single Pass Ordered Tree SPO-Tree that captures information with a single scan for incremental mining. All items in a transaction are inserted/sorted based on their frequency. The tree is reorganized dynamically when necessary. SPO-Tree allows for easy maintenance in an incremental or data stream environment.