| dc.description.abstract | Sequential pattern mining can reveal the behaviors of events along the time, and it is one of the most important approaches in data mining. In sequence data, events can be classified into two major types, where one is point-based events and the other is interval-based events. When a data sequence can contain both types of events, we say this data sequence has hybrid events. Recently, the work of Wu and Chen has proposed algorithms to discover sequential patterns of hybrid events, called hybrid temporal patterns. Although hybrid temporal patterns can reveal the temporal relationships among hybrid events, they can not tell us the time intervals among events. Therefore, this thesis extends hybrid temporal patterns so that the time intervals among events can be revealed. We call this type of extended patterns as time-interval hybrid temporal patterns. In the thesis, we proposed two algorithms, ti-HPrefixSpan and ti-HTPM, to discover time-interval hybrid temporal patterns by modifying traditional PrefixSpan and GSP algorithms, respectively. In the experiments, we evaluate the proposed methods’ performance using synthetic data and real stock price data. The results of the experiments show that the performances of the two proposed algorithms are quite satisfactory. Besides, we also show that time-interval hybrid temporal patterns can obtain higher prediction accuracy than hybrid temporal patterns. | en_US |