Location-Based Parallel Sequential Pattern Mining Algorithm

Citations

WEB OF SCIENCE

9
Citations

SCOPUS

11

초록

Given a data sequence, sequential pattern mining, which finds frequent sequence patterns among them, is an important data mining problem. However, in the existing sequential pattern mining, only the purchase order of the items is considered, and the position where the item is purchased is not considered. In this paper, we developed a sequential pattern mining algorithm using Apache spark. The proposed algorithm finds frequent sequential patterns in parallel by distributing data to several machines. Experimentally, we performed a comprehensive performance study on the proposed algorithm by varying various parameter values using various synthetic data. Experimental results show that the proposed algorithm shows a linear speed improvement over the number of machines.

키워드

Big data; MapReduce; PrefixSpan; sequential pattern mining
제목
Location-Based Parallel Sequential Pattern Mining Algorithm
저자
Kim, Byoungwook; Yi, Gangman
DOI
10.1109/ACCESS.2019.2939937
발행일
2019
유형
Article
저널명
IEEE Access
권
7
페이지
128651 ~ 128658