Incorporating boundary value concept and recency constraint to capture emerging trends in time stamp based sequence dataset

Sequential Pattern Mining (SPM) is an important subject to focuses on the current purchase scenario. Current research of SPM mostly emphasis on items or itemset which are frequently purchase in certain order but not focused on the purchase which will be potentially strong for future. In this paper, we have tried to focus on Emerging Patterns (EPs) based on significant change of its support values. Proposed algorithm worked on significant change of boundary value support threshold. There are some patterns which lies on boundary, due to its little law amount of frequency they are rejected, those patterns are not in consideration presently but which having similar kind of nature as discovered patterns. Incorporation of manipulation in Boundary value and Recency constraint in traditional SPM algorithm - PrefixSpan, helps to discover EPs and distinguish between actual infrequent patterns as well patterns suffering from little low support threshold from immense database. Proposed algorithm effectively discover the current spending patterns of customers and recent trends, which will allow decision maker to detect in a large database potential changes of customer preference, and provide as early as possible products and services desired by the customers to expand and retain business.