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.

Data Mining K-Means algorithm Clustering Sequential Pattern Mining Technique FP-Growth Algorithm Prefix Span Algorithm User behavior.