Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An efficient algorithm to update large itemsets with early pruning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
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
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
Extracting incidental and global knowledge through compact pattern trees in distributed environment
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
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Mining frequent pattern from databases is useful for knowledge discovery. In this paper, we propose modified CP-Tree, which scans entire transactions only once and constructs the tree by inserting the transactions one by one. The constructed tree consists of an item list along with its occurrence. In addition, a sorted order of items with its frequency of occurrence is maintained and based on the sorted value, the tree is dynamically rearranged. In rearranging phase, the nodes are rearranged in each branch based on sorted order of items. Each path of the branch is removed from the tree, sorted based on sorted order of items and inserted back as a branch into the tree. We have evaluated the performance of the proposed modified tree on benchmark databases such as CHESS, MUSHROOM and T10I4D100K. It is observed that the time taken for extracting frequent item from the tree is encouraging compared to conventional CP-Tree.