Earticle

초록

영어

Companies are building and utilizing their own data analysis systems according to business characteristics in the distributed cloud. However, as businesses and data types become more complex and diverse, the demand for more efficient analytics has increased. In response to these demands, in this paper, we propose an unsupervised learning-based data analysis agent to which reinforcement learning is applied for effective data analysis. The proposal agent consists of reinforcement learning processing manager and unsupervised learning manager modules. These two modules configure an agent with k-means clustering on multiple nodes and then perform distributed training on multiple data sets. This enables data analysis in a relatively short time compared to conventional systems that perform analysis of large-scale data in one batch.

목차

Abstract
1. Introduction
2. Related Work
2.1 Multiple Reinforcement Agent
2.2 K-means Clustering
3. Proposed Agent
3.1 Cluster-based multi-agent environment in distributed environment
3.2 Configuration of Multi-Agent
3.3 Execution Structure
3.4 Implementation Algorithm
4. Experiments and Results
5. Conclusion
Acknowledgement
References

키워드

저자

• Seo-Yeon Gu [ Master, Department of Computer Science, Kwangwoon University, Korea. ]
• Seok-Jae Moon [ Professor, Department of Artificial Intelligence Institute of Information Technology, KwangWoon University, Korea ] Corresponding author
• Byung-Joon Park [ Professor, Department of Computer Science, Kwangwoon University, Korea. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380