Earticle

초록

영어

With the rapid development of E-Commerce, how to evaluate the E-Commerce sites accurately has become an important issue. However, to cluster E-Commerce sites correctly and accurately is not an easy thing based on characteristics of high dimensions and uneven density for E-Commerce sites. This leads to bad performance of the cluster result. To analyze 100 E-Commerce demonstration enterprises in 2013-2014 named by the Ministry of Commerce People’s Republic of China, this paper adopts a data mining approach of DBSCAN method. In the data preprocessing phase, it adopts factor analysis to reduce dimensionality. In the cluster phase, this paper implements an improved DBSCAN algorithm to process the uneven density data. Finally, this paper gives suggestions to these 100 E-Commerce enterprises based on experiment results.

목차

Abstract
 1. Introduction
 2. Data Collection and Factor Analysis
  2.1.Data Collection
  2.2. Index Variables Selection
  2.3. Factor Analysis
 3. DBSCAN for Uneven Density Data Processing
  3.1.Traditional DBSCAN Algorithm and Its Application
  3.2. Proposed Improved DBSCAN and its Application
 4. Results and Analysis
 5. Conclusion and Future Work
 Reference

키워드

저자

• Yongyi Cheng [ College of Computer Science and Technology, Jilin University, Jilin Nongxin Information Technology Service Co., Ltd. ]
• Yumian Yang [ School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, China ]
• Jianhua Jiang [ School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, China ] corresponding author
• GaoChao Xu [ College of Computer Science and Technology, Jilin University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605