Closed Constrained Gradient Mining in Retail Databases
IEEE Transactions on Knowledge and Data Engineering
Discovering frequent itemsets by support approximation and itemset clustering
Data & Knowledge Engineering
A data mining proxy approach for efficient frequent itemset mining
The VLDB Journal — The International Journal on Very Large Data Bases
ON DATA STRUCTURES FOR ASSOCIATION RULE DISCOVERY
Applied Artificial Intelligence
Summary queries for frequent itemsets mining
Journal of Systems and Software
A top down algorithm for mining web access patterns from web logs
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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In this paper, we propose a new framework for miningfrequent patterns from large transactional databases. Thecore of the framework is of a novel coded prefix-path treewith two representations, namely, a memory-based prefix-pathtree and a disk-based prefix-path tree. The disk-basedprefix-path tree is simple in its data structure yet rich ininformation contained, and is small in size. The memory-basedprefix-path tree is simple and compact. Upon thememory-based prefix-path tree, a new depth-first frequentpattern discovery algorithm, called P P-Mine, is proposedin this paper that outperforms FP-growth significantly. Thememory-based prefix-path tree can be stored on disk usinga disk-based prefix-path tree with assistance of the new codingscheme. We present efficient loading algorithms to loadthe minimal required disk-based prefix-path tree into mainmemory. Our technique is to push constraints into the loadingprocess, which has not been well studied yet.