Mining time-delayed association from event datasets

Speaker: LOO King Kong

Abstract

We study the problem of finding time-delayed associations among types of events from an event dataset. Based on the properties of time-delayed associations, we present an algorithm for the problem. Starting from "short" associations, the algorithm repeatedly form and evaluate "longer" associations from those found to be frequent in due course.