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.

中文翻译：

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HOVA-FPPM：使用哈希发生向量和Apriori方法在时间序列数据库中进行灵活的周期性模式挖掘

由于不重要事件的不规则发生，因此在时间序列数据库中查找灵活的周期性模式并非易事，这对于大型数据集来说很难处理或计算量大。存在各种基于Apriori，投影，树和其他技术的解决方案来挖掘这些模式。但是，存在大小恒定的树结构（即后缀树），在整个挖掘过程中，该树具有额外的信息存储在内存中，冗余和无效的模式生成，有限类型的灵活周期模式的挖掘以及遍历树数据结构进行模式发现的遍历，导致不可接受的空间和时间复杂度。为了克服这些问题，我们引入了一种基于Apriori方法的有效方法HOVA-FPPM，该方法具有散列的发生向量，可以找到所有类型的灵活周期模式。我们不依赖复杂的树结构，而是在哈希表中管理必要的信息，以便在挖掘过程中进行有效查找。我们测量了我们提出的方法的性能，并将结果与​​基准方法（即FPPM）进行了比较。结果表明，无论数据大小或周期值如何，我们的方法都需要较少的时间和空间。