A novel multi-core algorithm for frequent itemsets mining in data streams

Data streams are modern data sources that are gaining attention as a consequence of their many practical applications (they can be found in data transmission, eCommerce, and intrusion detection system among others). Nevertheless, the efforts to obtain insights from data streams are limited due to their massive information volume and the time needed to process them. In this paper, a new approach for Frequent Itemsets Mining on data streams based on prefix trees which takes advantage of multi-core systems is proposed. This approach uses the Gearman framework as the interface for multi-core processing, and it allows to exploit their scalability efficiently. Experimental results show that the proposed method obtains the same patterns compared with similar approaches reported in the state-of-the-art and outperforms them concerning the processing time required. Also, it is proved that the proposed method is insensitive to variations in the support threshold value, and its efficiency depends on the size of the transactions and not on the size of the alphabet, which is a significant issue in other Frequent Itemsets Mining algorithms. (C) 2019 Elsevier B.V. All rights reserved.