Parallel algorithm research of graph search and depth learning based on data mining

DOI

10.2991/meita-16.2017.44How to use a DOI?

Keywords

Data mining; graph search; width-first search; depth learning; depth belief network

Abstract

At present, information technology presents exponential growth characteristics, it have entered the era of large data. Data is a strategic resource as important as self-heating resources and human resources, which implied huge economic value. How to effectively organize and deal with large data will play a huge role in the socio-economic development. The graph search and depth learning algorithms play a more and more important role in the processing of large data because of their strong ability of network analysis and feature recognition and classification. In this paper, we propose a parallel optimization method based on locality principle, synchronization cost reduction and load balancing to solve the problem of width-first search. Finally, this paper combines all the methods together, and proposes a width-first search method using heuristic search. The experimental results showed that the width-first search algorithm with parallel optimization has good acceleration effect.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Liping Wang
PY  - 2017/02
DA  - 2017/02
TI  - Parallel algorithm research of graph search and depth learning based on data mining
BT  - Proceedings of the 2016 2nd International Conference on Materials Engineering and Information Technology Applications (MEITA 2016)
PB  - Atlantis Press
SP  - 210
EP  - 214
SN  - 2352-5401
UR  - https://doi.org/10.2991/meita-16.2017.44
DO  - 10.2991/meita-16.2017.44
ID  - Wang2017/02
ER  -