The Optimization of Parallel Frequent Pattern Growth Algorithm Based on Mahout in Cloud Manufacturing Environment

Author

Jie Wang ; Yu Zeng

Author_Institution

Sch. of Manage., Capital Normal Univ., Beijing, China

Volume

2

fYear

2014

Firstpage

420

Lastpage

423

Abstract

In cloud manufacturing environment, many manufacturing enterprises will produce massive data of a variety of forms. We do research of optimization parallel frequent pattern mining algorithm based on Mahout in this paper. We first analyze the implement and defects of PFP-Growth in Mahout. Then we propose two optimization strategies. One is parallel sequence optimization, and another is optimization the storage of counting information. Datasets from real manufacturing and Webdocs show the effectiveness of the strategy in time and space of the optimization.

Keywords

cloud computing; data mining; manufacturing industries; optimisation; parallel algorithms; production engineering computing; Mahout; PFP-growth; Webdocs; cloud manufacturing environment; manufacturing enterprises; parallel frequent pattern growth algorithm optimization; parallel frequent pattern mining algorithm optimization; parallel sequence optimization; Algorithm design and analysis; Data mining; Distributed databases; Joints; Manufacturing; Optimization; Sorting; Cloud Manufacturing; Mahout; MapReduce; Parallel Frequent Pattern Growth;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on

Print_ISBN

978-1-4799-7004-9

Type

conf

DOI

10.1109/ISCID.2014.258

Filename

7082021