Abstract: Extracting predominant pattern in a time series database is a major data mining problem with several applications. The existing closed sequential patterns permit us to improve efficiency without bringing down the accuracy. The narrative technique developed a previous research follows a multiplex tree pruning technique which combines both the prefix and suffix tree patterns in an activity normalized time periodicity data sequences. The combinatorial point of prefix and suffix trees is on the threshold of predominant data pattern occurrence rate which efficiently identify the regularity of all observed patterns but still obtains the interlaced unwanted data. To separate the interlaced unwanted data from the predominant pattern mining, researchers are going to implement a new technique termed Optimized Discrete Interested Pattern technique (ODIP). This technique identifies the optimal value using the repetition occurrence in the pattern. An analytical and empirical result offers an efficient and effective predominant pattern mining framework for highly dynamic online time series data. Performance of the optimized discrete interested pattern technique is measured in terms of interlaced data removal efficiency, time taken for online pattern mining based on the frequency. Experiments are conducted with online time series data obtained from research repositories of both synthetic and real data sets.