A horizontal partitioning‐based method for frequent pattern mining in transport timetable

Abstract

AbstractAnalysing transport timetables is an important task, as it brings the opportunity to discover which routes commonly lead to delays. Frequent pattern mining is a technique used to support such type of discovery. However, functional dependencies are intrinsic properties present in timetables, particularly related to attributes derived from the origin–destination matrix. Such functional dependencies compromise the search for patterns in timetables in both the number of association rules (ARs) generated and the computational cost. Several of these ARs refer to the same information. Redundancy removal techniques can reduce the number of ARs. However, these techniques are designed to be used after mining finishes, which increases the computational cost of finding useful ARs. This work presents timetable pattern mining (T‐mine), a novel method for frequent pattern mining that improves knowledge discovery in timetables. We evaluated T‐mine using Brazilian Flight Data and compared T‐mine with the direct application of frequent pattern mining approaches with and without functional dependencies. Our experiments indicate that T‐mine is about one order magnitude faster than other methods with functional dependencies.