Existing temporal pattern mining assumes that events do not have any duration. However, events in many real world

applications have durations, and the relationships among these events are often complex. These relationships are mod-

eled using a hierarchical representation that extends Allen's interval algebra. However, this representation is lossy as the

exact relationships among the events cannot be fully recovered. In this paper, we augment the hierarchical representa-

tion with additional information to achieve a lossless representation. An efficient algorithm called IEMiner is designed

to discover frequent temporal patterns from interval-based events. The algorithm employs two optimization techniques

to reduce the search space and remove non-promising candidates. From the discovered temporal patterns, we build

an interval-based classifier called IEClassifier to differentiate closely related classes. Experiments on both synthetic

and real world datasets indicate the efficiency and scalability of the proposed approach, as well as the improved accuracy

of IEClassifier.