A new approximate method for mining frequent itemsets from big data

Computer Science and Information Systems

article

Timur Valiullin¹  Zhexue Joshua Huang¹  Chenghao Wei¹  Jianfei Yin¹  Dingming Wu¹  Iuliia Egorova¹ 

[1] Big Data Institute, College of Computer Science and Software Engineering Shenzhen University 518000 Shenzhen

关键词: approximate method;    frequent itemsets mining;    random sample partition;    big transaction database;   

DOI  :  10.2298/CSIS200124015V

学科分类：土木及结构工程学

来源: Computer Science and Information Systems

【 摘 要 】

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.

【 授权许可】

CC BY-NC-ND