Building an online purchasing behavior analytical system with neural network
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | Data Mining III |
| Pages | 225-237 |
| Publication status | Published - 2002 |
Publication series
| Name | Management Information Systems |
|---|---|
| Volume | 6 |
Conference
| Title | Third International Conference on Data Mining, Data Mining III |
|---|---|
| Place | Italy |
| City | Bologna |
| Period | 25 - 27 September 2002 |
Link(s)
Abstract
A back-propagation neural network (BPNN) classification model based on the data mining process procedures to analyze online customer behavior was presented. It was demonstrated that BPNN performs very well against two traditional statistical classification technologies which are K-means clustering and multiple discriminate analysis (MDA) on real-world data sets. The reliability and validity of the BPNN system were tested by comparing their classification ability with two different data sets which include an external statistical data set and an online survey data set. The results showed that the BPNN classification model has a higher classification accuracy and robustness than the alternatives used.
Citation Format(s)
Building an online purchasing behavior analytical system with neural network. / Wang, Mo; Rees, S. J.; Liao, S. Y.
Data Mining III. 2002. p. 225-237 (Management Information Systems; Vol. 6).
Data Mining III. 2002. p. 225-237 (Management Information Systems; Vol. 6).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
