Mining frequent itemsets in transaction databases is an important task in
many applications. It becomes more challenging when dealing with a large
transaction database because traditional algorithms are not scalable due
to the memory limit. In this paper, we propose a new approach for
approximately mining of frequent itemsets in a big transaction database.
Our approach is suitable for mining big transaction databases since it
produces approximate frequent itemsets from a subset of the entire
database, and can be implemented in a distributed environment. Our
algorithm is able to efficiently produce high-accurate results, however it
misses some true frequent itemsets. To address this problem and reduce
the number of false negative frequent itemsets we introduce an additional
parameter to the algorithm to discover most of the frequent itemsets
contained in the entire data set. In this article, we show an empirical
evaluation of the results of the proposed approach.
TRICE: Mining Frequent Itemsets by Iterative TRimmed Transaction LattICE in Sparse Big Data