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An Effective Approach to Mine Frequent Sequential Pattern over Uncertain dataset

In recent years, due to the wide applications of uncertain data, mining frequent itemsets over uncertain databases has attracted much attention. In uncertain databases, the support of an itemset is a random variable instead of a fixed occurrence counting of this itemset. There are several application in which the data mining on uncertain data is useful like sensor network monitoring, moving object search etc. In this paper we are focusing on mining frequent sequential pattern using SeqU-PrefixSpan algorithm. Using this algorithm we can find the frequent sequential pattern from an uncertain database. We are proposing one incremental approach for this algorithm, which will also find the patterns and reduce the time of execution by dividing the data into parts. Data input will be in parts which are done randomly. Due to this the execution time will be less as compared to existing algorithm. Keywords— Sequential pattern mining, uncertain data