An Efficient Tree-based Frequent Temporal Inter-object Pattern Mining Approach in Time Series Databases

Abstract

In order to make the most of time series present in many various application domains such as finance, medicine, geology, meteorology, etc., mining time series is performed for useful information and hidden knowledge. Discovered knowledge is very significant to help users such as data analysts and managers get fascinating insights into important temporal relationships of objects/phenomena along the time. Unfortunately, two main challenges exist with frequent pattern mining in time series databases. The first challenge is combinatorial explosion of too many possible combinations for frequent patterns with their detailed descriptions and the second one is to determine frequent patterns truly meaningful and relevant to the users. Therefore, in this paper, we proposed tree-based frequent temporal inter-object pattern mining algorithm to cope with these two challenges in a level-wise bottom-up approach. In comparison with the existing works, our proposed algorithm is more effective and efficient for frequent temporal inter-object patterns which are more informative with explicit and exact temporal information automatically discovered from a time series database. As shown in experiments on real financial time series, our work has reduced many invalid combinations for frequent patterns and also avoided many irrelevant frequent patterns returned to the users.