Knowledge discovery of weighted RFM sequential patterns with multi time interval from customer sequence database

Abstract

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 recent and profitable. So, RFM-based sequential pattern mining techniques is introduced. Although RFM-based sequential pattern mining gives buying patterns which are recently active and profitable however it does not give the time interval between each and every items. To discover a time interval, RFM-TI algorithm is proposed. The advantages of considering multi time interval is, from that we are able to realize what customer would possibly buy in next “h” step rather than next step. The experimental evaluation shows that the proposed method can discover more valuable patterns than RFM-based sequential pattern mining.