Open AccessA trie-based APRIORI implementation for mining frequent item sequences
Author(s) -
Ferenc Bodon
Publication year - 2005
Publication title -
citeseer x (the pennsylvania state university)
Language(s) - English
Resource type - Conference proceedings
ISBN - 1-59593-210-0
DOI - 10.1145/1133905.1133913
Subject(s) - trie , computer science , apriori algorithm , a priori and a posteriori , data mining , cache , data structure , transactional leadership , theoretical computer science , parallel computing , association rule learning , programming language , psychology , social psychology , philosophy , epistemology
In this paper we investigate a trie-based APRIORI algorithm for mining frequent item sequences in a transactional database. We examine the data structure, implementation and algorithmic features mainly focusing on those that also arise in frequent itemset mining. In our analysis we take into consideration modern processors' properties (memory hierarchies, prefetching, branch prediction, cache line size, etc.), in order to better understand the results of the experiments.
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