An efficient approach to mine flexible periodic patterns in time series databases

Periodic pattern mining in time series databases is one of the most interesting data mining problems that is frequently appeared in many real-life applications. Some of the existing approaches find fixed length periodic patterns by using suffix tree structure, i.e., unable to mine flexible patterns. One of the existing approaches generates periodic patterns by skipping intermediate events, i.e., flexible patterns, using apriori based sequential pattern mining approach. Since, apriori based approaches suffer from the issues of huge amount of candidate generation and large percentage of false pattern pruning, we propose an efficient algorithm FPPM (Flexible Periodic Pattern Mining) using suffix trie data structure. The proposed algorithm can capture more effective variable length flexible periodic patterns by neglecting unimportant or undesired events and considering only the important events in an efficient way. To the best of our knowledge, ours is the first approach that simultaneously handles various starting position throughout the sequences, flexibility among events in the mined patterns and interactive tuning of period values on the go. Complexity analysis of the proposed approach and comparison with existing approaches along with analytical comparison on various issues have been performed. As well as extensive experimental analyses are conducted to evaluate the performance of proposed FPPM algorithm using real-life datasets. The proposed approach outperforms existing algorithms in terms of processing time, scalability, and quality of mined patterns. HighlightsDevised a new algorithm to generate flexible periodic patterns using suffix trie.Handling variable starting position for mining periodicity without recalculation.A new periodicity detection system to find more interesting periodic patterns.Mining periodicity in a single run and database scan in more interactive manner.Efficiency and scalability of proposed approach are tested with real life datasets.