Call for Paper - September 2022 Edition
IJCA solicits original research papers for the September 2022 Edition. Last date of manuscript submission is August 22, 2022. Read More

Customer Purchasing Behavior using Sequential Pattern Mining Technique

Print
PDF
International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 119 - Number 1
Year of Publication: 2015
Authors:
Ashish Goel
Bhawana Mallick
10.5120/21032-2939

Ashish Goel and Bhawana Mallick. Article: Customer Purchasing Behavior using Sequential Pattern Mining Technique. International Journal of Computer Applications 119(1):24-31, June 2015. Full text available. BibTeX

@article{key:article,
	author = {Ashish Goel and Bhawana Mallick},
	title = {Article: Customer Purchasing Behavior using Sequential Pattern Mining Technique},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {119},
	number = {1},
	pages = {24-31},
	month = {June},
	note = {Full text available}
}

Abstract

In this competitive world, wherever each organization or company must improve yourself for collaborating within the market. For this improvement, we'd like to grasp the client purchasing behavior [2]. Presently data processing provides several techniques to enhance it. Aim of this paper is to shortcoming of the "Frequent Pattern Mining Technique" and use the "Sequential Pattern Mining Technique" to enhance the client purchasing Behavior. Because Apriori generate millions of candidate sets [19] and scan the group action information repeatedly and FP-Growth generate the Large No. of Projected database. As we all know that one amongst the foremost fashionable data processing approaches is "Clustering Analysis", that is truly helpful for divide the information attribute into similar variety of teams that take the high intra similarity and low lay to rest similarity and "Sequential Pattern" technique to seek out the co-relations between attributes of a relation and have applications in promoting, monetary and retail sector and it's unremarkably applied to investigate market baskets to assist organizers to work out that things are consecutive purchased along by customers. This paper proposes an efficient technique to extract information from transactions records that is extremely helpful for increasing the client Satisfaction. Client details are divided victimization K-means [1] and sequential Pattern[4] Mining Technique "Prefix" algorithm is applied to spot client behavior over Banking knowledge. Firstly, we divide into the database into a n number of partition with the help of Clustering technique Then Sequential algorithm provide the sequential patterns over banking data of Indian bank. According to the result of the both technique, the processing time of mining is decreased and the efficiency of algorithm has improved.

References

  • A. K. Jain, June, 2010, "Data Clustering: 50 Years Beyond K-Means", Volume No. 31, Issue 8, pp: 651-666.
  • Abdullah Al-Mudimigh, 2009, "Efficient implementation of data mining: improve customer's behavior", pp 7-10.
  • Ayres, J. , 2002, "Sequential pattern mining using a bitmap representation", In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  • Agrawal R and Srikant R, 1995, "Mining Sequential Patterns", Volume No. 9.
  • BharatiR. Jipkate, 2012 "A Comparative Analysis of Fuzzy C-Means Clustering and K Means Clustering Algorithms", pp: 2250–3005.
  • Dawn E. Holmes, 2012, "Data Mining Techniques in Clustering, Association and Classification, Data Mining", Volume 23, pp: 1-6.
  • Gianfranco Chicco, MAY (2006), Comparisons Among Clustering Techniques for Electricity Customer Classification", Volume No. 21, Issue No. 2, pp: 933-940.
  • Han J. , Dong G. , 2000 "Free-span: Frequent pattern-projected sequential pattern mining", pp: 355-359.
  • Horng-Jinh Chang, 2007 "An anticipation model of potential customers' purchasing behavior based on clustering analysis and association rules analysis" Volume No. 32, pp: 753–764
  • Jian. Pei, 2001, "Prefix-Span: Mining Sequential Patterns Efficiently by Prefix- Projected Pattern Growth".
  • Lijuan Huang, DECEMBER (2011), "Analysis on E-consumers' Purchasing Behavior Based on Data-driving Model", Volume No. 6, Issue No. 12, pp: 1713-1717.
  • M. Zaki, 2001 "SPADE: An efficient algorithm for mining frequent sequences, Machine Learning,.
  • P. Isakki alias Devi, (2012), " Analysis of Customer Behavior using Clustering and Association Rules " Volume 43– No. 23, pp 0975 – 8887.
  • Rakesh Agrawal and RamakrishnanSrikant, (1994) "Fast algorithms for mining association n rules in large databases". Volume No. 20, pp: 487-499.
  • Srikant R. and Agrawal R. , (1996), "Mining sequential patterns: Generalizations and performance improvements", Volume No. 5.
  • Shruti Aggarwa, 2008"Comparative Study of Various Improved Versions of Apriori Algorithm".
  • Shalini S Singh, 13-14 May 2011 "K-means v/s K-medoids: A Comparative Study".
  • T. Velmurugan, 2011, "A Survey of Partition based Clustering Algorithms in Data Mining: An Experimental Approach", Volume No. 10, Issue No . 3, pp: 478- 484.
  • V. Thanuja, 2011, "Applications of Data Mining in Customer Relationship Management", Volume No. 2, Issue 3, pp: 423-433