Temporal association rules discovery algorithm based on improved index tree

With the rapid increase of information generated from all kinds of sources, temporal big data mining in business area has been paid more and more attention recently. A novel data mining algorithm for mining temporal association is proposed. Mining temporal association can not only provide better predictability for customer behaviour but also help organisations with better strategies and marketing decisions. To compare the proposed algorithm, two methods to mine temporal association are presented. One is improved based on a traditional mining algorithm, Apriori. The other is based on an Index-Tree. Moreover, the proposed method is extended to mine temporal association in multi-dimensional space. The experimental results show that the Index-Tree method outperforms the Apriori-modified method in all cases.

Data Mining as Support for Customer Relationship Management: A Case Study

This article describes the data mining application to CRM - Customer Relationship Management. The article starts with an introduction showing the importance of the CRM strategy for the company, after it’s introduced the theoretical about CRM, Knowledge Discovery Database and its stages, with emphasis to the mining stage and concludes with presentation of a case study and the conclusions. For the case study it was developed a prototype of an information system of a bookstore, it was implemented, beyond the conventional functions, the association rules discovery algorithm. The implementation of the data mining technique allowed to the system supply support so that the user knows better the client, becoming possible the application of the strategy of CRM.