A New Approximate Method For Mining Frequent Itemsets From Big Data *

Frequent itemsets mining is the first and most critical step of finding association rules from a transaction database. Association rules mining is one of the main data mining tasks in many applications, such as basket analysis [3], product recommendation [20], crossselling [10], etc. Huge research efforts have been devoted to solving frequent itemsets mining problem. Many of these studies had considerable impact and led to a plenty of sophisticated and efficient algorithms for association rules mining, such as Apriori [1,2], 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 limited main memory. In this paper, we propose a new approach for the approximately mining of frequent itemsets in a big transaction database. Our approach is suitable for mining big transaction databases since it uses the frequent itemsets from a subset of the entire database to approximate the result of the whole data, 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.

Automated summary

Frequent itemsets mining is a critical step in finding association rules from transaction databases, and various efficient algorithms have been proposed for this task.