Mining Temporal Patterns with Quantitative Intervals

TLDR

Quantitative temporal information is fundamental in many contexts and is incorporated into mining processes. The study aims to discover frequent temporal patterns in databases of temporal sequences with dates and durations. The authors adapt the a priori framework to an efficient algorithm using a hyper‑cube representation of temporal sequences, extracting quantitative temporal information via density estimation of event‑interval distributions. Evaluation on synthetic data demonstrates that the algorithm robustly extracts frequent temporal patterns with quantitative temporal extents.

Abstract

In this paper we consider the problem of discovering frequent temporal patterns in a database of temporal sequences, where a temporal sequence is a set of items with associated dates and durations. Since the quantitative temporal information appears to be fundamental in many contexts, it is taken into account in the mining processes and returned as part of the extracted knowledge. To this end, we have adapted the classical a priori (Agrawal and Srikant, 1995) framework to propose an efficient algorithm based on a hyper-cube representation of temporal sequences. The extraction of quantitative temporal information is performed using a density estimation of the distribution of event intervals from the temporal sequences. An evaluation on synthetic data sets shows that the proposed algorithm can robustly extract frequent temporal patterns with quantitative temporal extents.