Name
Title | ||
|---|---|---|
Using linear discriminant analysis and data mining approaches to identify E-commerce anomaly |
Abstract | ||
|---|---|---|
Electronic commerce has been rather pervasive in our life today. However, the damage is equally pervasive. For Business to Consumer type of E-commerce, various types of E-commerce anomaly usually incurs loss of revenue, reduced customer satisfaction and compromised business confidentiality. This paper proposes linear discriminant analysis and data mining approaches to identify the E-commerce anomaly. The data mining approaches yield superior performance. However, the unbalanced data make the data mining approaches dominated by the data of the majority class. LDA is introduced to deal with the unbalanced data set. The results indicate that our proposed methods can identify the E-commerce anomaly precisely. The practice insights from the results are also given. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICNC.2011.6022591 | ICNC |
Keywords | Field | DocType |
reduced customer satisfaction,data mining approaches,linear discriminat analysis,e-commerce anomaly identification,bagging,baynesnet,e-commerce anomaly,linear discriminant analysis,revenue loss,customer satisfaction,data mining,electronic commerce,compromised business confidentiality,security of data,business,sensitivity,e commerce,testing,accuracy | Revenue,Data mining,Customer satisfaction,Consumer-to-business,Confidentiality,Computer science,Artificial intelligence,Linear discriminant analysis,E-commerce,Machine learning | Conference |
Volume | Issue | ISSN |
4 | null | 2157-9555 |
ISBN | Citations | PageRank |
978-1-4244-9950-2 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zijiang Yang | 1 | 355 | 34.71 |
| Shouxin Cao | 2 | 0 | 0.34 |
| Bo Yan | 3 | 49 | 7.88 |