Discovery of Quantitative Sequential Patterns from Event Sequences

Abstract

In this paper, we consider the problem of frequent pattern mining in databases of temporal events with intervals. Since quantitative temporal information might play important roles in many application domains, it is critical to discover patterns to which numerical attributes are associated. To this end, we consider two kinds of temporal patterns with quantitative information on the durations and time differences of events, and propose corresponding algorithms by incorporating numerical clustering techniques into existing temporal pattern miners. The effectiveness of the proposed algorithms was assessed by using real world datasets.