Design of customer marketing big data processing system based on data mining clustering technology
- DOI
- 10.2991/ameii-16.2016.20How to use a DOI?
- Keywords
- Data mining, Hadoop, MapReduce, Hbase
- Abstract
Data mining technology brings together databases, artificial intelligence, machine learning, statistics, visualization, parallel computing in different fields, to build their own methodology. Use database technology for front-end data processing, application of machine learning methods to extract useful knowledge from the data processed, the data and to analyze the characteristics and trends behind the final data given about the overall characteristics and trends. Use of visualization techniques the human observation and intelligence into the system, with an intuitive graphical information mode, association or trend data presented to decision-makers, enabling users to interactively analyze data. Customer relationship management is an important means to maintain market competitiveness and indispensable part. The introduction of data mining technology to achieve the goal of high-quality customer relationship management, give full play to the role of customer relationship management. At present, many foreign companies in order to gain a competitive advantage, actively engaged in the study of human and material resources and applications, and achieved a better return on investment.
- Copyright
- © 2016, 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/).
Cite this article
TY - CONF AU - Jingzhe Wang PY - 2016/04 DA - 2016/04 TI - Design of customer marketing big data processing system based on data mining clustering technology BT - Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) PB - Atlantis Press SP - 100 EP - 104 SN - 2352-5401 UR - https://doi.org/10.2991/ameii-16.2016.20 DO - 10.2991/ameii-16.2016.20 ID - Wang2016/04 ER -