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
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
A Support-Ordered Trie for Fast Frequent Itemset Discovery
IEEE Transactions on Knowledge and Data Engineering
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
Incrementally fast updated frequent pattern trees
Expert Systems with Applications: An International Journal
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
SMARViz: Soft Maximal Association Rules Visualization
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
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
A soft set approach for association rules mining
Knowledge-Based Systems
Mining significant least association rules using fast SLP-growth algorithm
AST/UCMA/ISA/ACN'10 Proceedings of the 2010 international conference on Advances in computer science and information technology
IVIC'11 Proceedings of the Second international conference on Visual informatics: sustaining research and innovations - Volume Part I
A fast algorithm for maintenance of association rules in incremental databases
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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One of the popular and compact trie data structure to represent frequent patterns is via frequent pattern tree (FP-Tree). There are two scanning processes involved in the original database before the FP-Tree can be constructed. One of them is to determine the items support (items and their support) that fulfill minimum support threshold by scanning the entire database. However, if the changes are suddenly occurred in the database, this process must be repeated all over again. In this paper, we introduce a technique called Fast Determination of Item Support Technique (F-DIST) to capture the items support from our proposed Disorder Support Trie Itemset (DOSTrieIT) data structure. Experiments through three UCI benchmark datasets show that the computational time to capture the items support using F-DIST from DOSTrieIT is significantly outperformed the classical FP-Tree technique about 3 orders of magnitude, thus verify its scalability.