Name
Abstract | ||
|---|---|---|
Understanding and adapting to changes of customer behavior is an important aspect for a internet-based company to survive in a continuously changing environment. The aim of this paper is to develop a methodology which detects changes of customer behavior automatically from customer profiles and sales data at different time snapshots. For this purpose, we first define the three types of changes as emerging pattern, unexpected change and the added/perished rule, then, we develop similarity and difference measures for rule matching to detect all types of change. Finally, the degree of change is evaluated to detect significantly changed rules. Our proposed methodology can evaluate the degree of changes as well as detect all kinds of change automatically from different time snapshot data. A case study on an internet shopping mall for evaluation of this methodology is also provided. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1016/S0957-4174(01)00037-9 | Expert Systems with Applications |
Keywords | Field | DocType |
Data mining,Association rule mining,Change mining | Customer intelligence,Data mining,Consumer behaviour,Computer science,Rule matching,Association rule learning,Internet shopping,Snapshot (computer storage),The Internet,Change mining | Journal |
Volume | Issue | ISSN |
21 | 3 | 0957-4174 |
Citations | PageRank | References |
75 | 3.32 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hee Seok Song | 1 | 157 | 8.75 |
| Jae Kyeong Kim | 2 | 1011 | 52.32 |
| Soung Hie Kim | 3 | 1195 | 77.55 |