Constraint Fuzzy Sequential Pattern Mining with TOPSIS Method

Abstract

To maintain profitability, many companies consider effective customer relationship management (CRM) to be one of the critical factors for success. The central objective of CRM is to maximize the lifetime value of a customer to a company and find positive customers. One of the methods that help this is sequential pattern mining. Sequential pattern mining is to discover all sub-sequences that are frequent. The classical sequential pattern mining algorithms do not allow processing of numerical data and require preprocessing of these data into a binary representation, which necessarily leads to a loss of information. Fuzzy sets are used to overcome this problem. In present fuzzy sequential pattern mining algorithms, there isn't any matter of itemset time and sequences are only found based on sequence of happening. In this paper, a novel algorithm about fuzzy sequential pattern mining is proposed with the time-gap constraints confine the time interval between two adjacent elements to a reasonable period while the sliding time window constraint permits elements of a pattern to span a set of transactions within a user-specified window and a fuzzy membership function is considered. Therefore, loss of useful sequences is prevented in the search process. The proposed algorithm searches for a goal sequence within the defined fuzzy sliding window and fuzzy gap functions.