원문정보
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
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