Abstract Sequential pattern mining is advantageous for seve ral applications. For example, it finds out the sequential purchasing behavior of majority customers from a la rge number of customer transactions. However, the e xisting researches in the field of discovering sequential patterns are ba sed on the concept of frequency and presume that th e customer purchasing behavior sequences do not fluctuate with change in time, purchasing cost and other parameters. To accl imate the sequential patterns to these changes, constraint are integrate d with the traditional sequential pattern mining ap proach. It is possible to discover more user)centered patterns by integrating certain constraints with the sequential mining process. Thus in this paper, monetary and compactness constraints in addition to frequency and length are included in the sequential mining process for discovering pertinent sequential patterns from sequ ential databases. Also, a CFML)PrefixSpan algorithm is proposed by integrating these constraints with the original Pre fixSpan algorithm, which allows discovering all CFM L sequential patterns from the sequential database. The proposed CFML)Pre fixSpan algorithm has been validated on synthetic sequential databases. The experimental results ensure that the efficacy of the sequential pattern mining process is further enhanced in view of the fact that the purchasing cost, time dur ation and length are integrated with the sequential pattern mining process.

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[31] Zhao Q. and Bhowmick S., Sequential Pattern Mining: A Survey, Technical Report, Nanyang Technological University, Singapore, 2003. 42 The International Arab Journal of Information Technology, Vol. 11, No. 1, January 2014 Bhawna Mallick received her B.Tech in a computer technology from Nagpur University, India and M.Tech in information technology from Punjabi University, Patiala, India. She is currently, pursuing PhD degree and working as head, Department of Computer Science & Engineering at Galgotias College of Engineering & Technology, Greater Noida affiliated to UP Technical University , India. She has 13 years of industry and academic experience with organizations like Infosys Technologies Ltd, India and NIIT Technologies Ltd, India. She is a member of IEEE. Her researched interest is data mining focusing on sequential mini ng of progressive databases. Deepak Garg received his PhD in the area of efficient algorithm design from Thapar University. He is certified on latest technologies from Sun for Java Products, IBM for web services and brain bench for programming concepts. He is senior member of IEEE, USA, executive member of IEEE Delhi section and secretary of IEEE Computer Societ y, Delhi Section. He is life member of ISTE, CSI, IETE , ISC, British Computer Society and ACM, UK. He started his career as a software engineer in IBM Corporation Southbury, CT, USA and then with IBM Global Services India Pvt Ltd, India. He is presently, working as professor, Thapar University. He has 37 publications in international journals and conferences. He is a member of the Editorial Board of seven International Journals. His active research area is data structure, algorithms and data mining. Preetam Singh Grover received his Ms degree and PhD from Delhi University, India. He is widely travelled and delivered invited talks/key note addresses at many National/International Conferences/ Seminars and Workshops. He is on the Editorial Board of four International Journals. He has written 9 books and many of his articles have appeared in several books published by IEEE of USA. He has published more than 100 research papers in International and National Journals and Conferences including published by IEEE, ACM and Springer. He is presently, Director General at Guru Tegh Bahadur Institute of Technology, GGS Indraprastha Universit y, India. Formerly, he was dean and head of Computer Science Department, Delhi, India. Prof Grover is a member of IEEE Computer Society. His current research interests are: component based and aspect6 oriented software engineering, autonomic embedded systems.