Abstract: Discovering specified patterns in a time series database has expected much consideration and is nowadays a comparatively mature field. Existing Periodic Pattern Mining algorithms concentrates on mining which involves subsequences. However, huge portion of requests for example, genetic DNA and protein pattern mining requires estimated patterns that are adjacent in nature. The existing algorithms applied to discover such estimated pattern mining comprises of complicated problems such as deprived scalability and complexity while applying towards certain other applications. To overcome these limitations, a novel technique is presented that evolves a set of periodic pattern if the regularity of the occurrence changes from that estimated pattern. The technique is based on the combination of both suffix and prefix tree patterns, to develop a multiplex tree pruning, for an activity normalized time periodicity data sequences. The integrative sequence of prefix and suffix trees is based on the threshold factor of predominant data pattern occurrence rate. The conceptual model of multiplex tree pruning technique presented in this study, in combination with the prefix and suffix tree model for pruning items identifies the regularity of all observed patterns in an efficient manner. The detailed experimental study shows strong gains in periodic pattern mining, ensure fast storage of all the time series for a specified item. Empirical studies with varied time series data obtained from bank and car data set using UCI repositories is measured and evaluated in terms of time efficiency of pruning patterns of interest, sensitivity and accuracy.