Knowledge discovery of weighted RFM-Q sequential patterns from customer sequence database

Sequential pattern mining is helpful methodology to discover customer purchasing behaviour from large sequence database. Sequential pattern mining can be used in medical records, marketing, sales analysis, and web log analysis and so on. The traditional sequential pattern mining does not give the pattern which is actively recent and profitable. So, RFM-based sequential pattern mining techniques is introduced. Although RFM-based sequential pattern mining gives buying patterns which are recent and profitable but it does not gives the quantity of items in buying pattern. RFM-Q algorithm is proposed to discover quantity of items which is purchased by customer. The advantages of considering quantity is that company can use it for providing a sales promotion. The experimental evaluation shows that the proposed method can discover more valuable patterns than RFM-based sequential pattern mining.