PERFORMANCE COMPARISON BETWEEN PATTERN GROWTH ALGORITHMS FOR MINING SEQUENTIAL PATTERN

Sequential Pattern Mining is very important concept in Data Mining, finds frequent patterns from given sequence. It is used in various domains such as medical treatments, customer shopping sequence, DNA sequence and gene structures. Sequential Pattern Mining Approaches are classified into two categories: Apriori or generate and test approach, pattern growth or divide and conquer approach. In this paper, we are introducing a more efficient algorithm for sequential pattern mining. The time & space consumption of proposed algorithm will be lesser in comparison to previous algorithms & we compare two algorithms of pattern growth algorithms of Sequential Pattern Mining, one is P-prefix span which discovers frequent sequential pattern with probability of inter arrival time and other one is new proposed algorithm named as Percussive algorithm. Our experiment shows that new proposed algorithm is more efficient and scalable then the P-prefix span algorithm. Keywords: Data Mining, Sequential Pattern Mining, Frequent Item set, Support count, Sequence database.