Efficient constraint-based Sequential Pattern Mining (SPM) algorithm to understand customers’ buying behaviour from time stamp-based sequence dataset

Business Strategies are formulated based on an understanding of customer needs. This requires development of a strategy to understand customer behaviour and buying patterns, both current and future. This involves understanding, first how an organization currently understands customer needs and second predicting future trends to drive growth. This article focuses on purchase trend of customer, where timing of purchase is more important than association of item to be purchased, and which can be found out with Sequential Pattern Mining (SPM) methods. Conventional SPM algorithms worked purely on frequency identifying patterns that were more frequent but suffering from challenges like generation of huge number of uninteresting patterns, lack of user’s interested patterns, rare item problem, etc. Article attempts a solution through development of a SPM algorithm based on various constraints like Gap, Compactness, Item, Recency, Profitability and Length along with Frequency constraint. Incorporation of six additional constraints is as well to ensure that all patterns are recently active (Recency), active for certain time span (Compactness), profitable and indicative of next timeline for purchase (Length―Item―Gap). The article also attempts to throw light on how proposed *Corresponding author: Niti Ashish Kumar Desai, Department of Computer Engineering, Uka Tarsadia University, Bardoli, Surat, Gujarat, India E-mail: [email protected] Reviewing editor: Hsien-Tsung Chang, Chang Gung University, Taiwan Additional information is available at the end of the article ABOUT THE AUTHORS Niti Ashish Kumar Desai has received her BE in Computer Engineering from SGU, Gujarat, India in 2003 and ME from Dharmsinh Desai University, Gujarat, India in 2005. She has joined her PhD in the area of Data Mining at Uka Tarsadia University, Bardoli, Gujarat in June 2012. Her areas of interest include Database and Data Mining. During her 8+ years of academic journey she has published more than 10 papers in various National and International Journals. Dr. Amit Ganatra has received his BE and M.E. in 2000 and 2004 from DDIT-Nadiad, Gujarat. He has completed Ph.D. in Information Fusion Techniques in Data Mining from KSV University, Gandhinagar, Gujarat. He is a member of IEEE and CSI. He has 15+ years of teaching and research experience. He has published and contributed over 100+ papers. He is concurrently holding Professor, headship in computer Department and Deanship in Faculty of Technology-CHARUSAT, Gujarat. PUBLIC INTEREST STATEMENT The main objective of a business economic activity is to satisfy needs and wants of customers. Hence management of any business aims at identifying and predicting purchase tendency of customers with a view to plan its business strategy including product development and marketing substrategies. This requires data mining tools including sequential pattern mining (SPM) techniques that help in achieving aforesaid objective. Most of the existing SPM approaches work purely on frequency, which fail to extract sequential patterns of users’ interest. Incorporation of constraints in SPM is able to address such shortcomings. Proposed framework might be useful for decision-maker to understand business, to identify past, current as well future buying pattern of customers. Further Emerging Patterns (EPs) that can help in predicting future buying behaviour can also be identified with the proposed framework. Paper also highlights obsolete, new and forming stage patterns that will be relevant for business managements. Received: 14 April 2015 Accepted: 03 July 2015 Published: 14 September 2015 © 2015 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Page 1 of 20 Niti Ashish Kumar Desai