An algorithm for mining closed weighted sequential patterns with flexing time interval for medical time series data

In this paper, an algorithm for mining the nonredundant closed weighted sequential patterns with flexible time intervals for the medical time series data is proposed. Initially, the sequence weight for each sequence is calculated based on the time interval between the itemsets and subsequently the candidate sequences are generated with flexible time intervals. The next step is, computation of frequent sequential patterns with the aid of proposed support measure and subsequently the frequent sequential patterns are subjected to closure checking process which leads to filter the closed sequential patterns with flexible time intervals. Finally, the proposed methodology produces a necessary sequential patterns which is proved. The proposed methodology constructs closed sequential patterns which are 23.2% lesser than the sequential patterns.