Abstract

Electronic Commerce (EC) has offered a new channel for instant on-line shopping. However, there are too many various products available from a great number of virtual stores on the Internet for Internet shoppers to select. On-line one-to-one marketing therefore becomes a great assistance to Internet shoppers. One of the most important marketing resources is the prior daily transaction records in the database. The great amount of data not only gives the statistics, but also offers the resource of experiences and knowledge. It is quite natural that marketing managers can perform data mining on the daily transactions and treat the shoppers the way they prefer. However, the data mining on a significant amount of transaction records requires efficient tools. Data mining from automatic or semi-automatic exploration and analysis on a large amount of data items set in a database can discover significant patterns and rules underlying the database. The knowledge can be equipped in the on-line marketing system to promote Internet sales.

The purpose of this paper is to develop a mining association rules procedure from a database to support on-line recommendation. By customers and products fragmentation, product recommendation based on the hidden habits of customers in the database is therefore very meaningful. The proposed data mining procedure consists of two essential modules. One is a clustering module based on a neural network, Self-Organization Map (SOM), which performs affinity grouping tasks on a large amount of database records. The other rule is extraction module employing rough set theory that can extract association rules for each homogeneous cluster of data records and the relationships between different clusters. The implemented system was applied to a sample of sales records from a database for illustration.

Author Keywords: Data mining; SOM; Rough set; Association rules; On-line marketing

Article Outline

1. Background and motivation

2. A proposed data mining procedure

2.1. Step 1—selection and sampling

2.2. Step 2—transformation and normalization

2.3. Step 3—data mining of association rules

2.3.1. Clustering module

2.3.2. Rule extraction module

2.4. Characterization of each cluster

2.5. Association of different clusters

3. Application in EC and Discussions

4. Conclusions

Acknowledgements

References

Corresponding author. Fax: +886-4-3742337; email: swc@cyut.edu.tw