HOVA-FPPM: Flexible Periodic Pattern Mining in Time Series Databases Using Hashed Occurrence Vectors and Apriori Approach

Abstract

Finding flexible periodic patterns in a time series database is nontrivial due to irregular occurrence of unimportant events, which makes it intractable or computationally intensive for large datasets. There exist various solutions based on Apriori, projection, tree, and other techniques to mine these patterns. However, the existence of constant size tree structure, i.e., suffix tree, with extra information in memory throughout the mining process, redundant and invalid pattern generation, limited types of mined flexible periodic patterns, and repeated traversal over tree data structure for pattern discovery, results in unacceptable space and time complexity. In order to overcome these issues, we introduce an efficient approach called HOVA-FPPM based on Apriori approach with hashed occurrence vectors to find all types of flexible periodic patterns. We do not rely on complex tree structure rather manage necessary information in a hash table for efficient lookup during the mining process. We measured the performance of our proposed approach and compared the results with the baseline approach, i.e., FPPM. The results show that our approach requires lesser time and space, regardless of the data size or period value.