Open AccessA new approximate method for mining frequent itemsets from big data
Author(s) -
Timur Valiullin,
Zijing Huang,
Chunhong Wei,
Jianfei Yin,
Dingming Wu,
Iuliia Egorova
Publication year - 2021
Publication title -
computer science and information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.244
H-Index - 24
eISSN - 2406-1018
pISSN - 1820-0214
DOI - 10.2298/csis200124015v
Subject(s) - computer science , database transaction , scalability , data mining , big data , set (abstract data type) , task (project management) , transaction data , database , management , economics , programming language
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.