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

What is it about?

Frequent pattern mining is a technique used to support the discovery of which routes commonly lead to delays. 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.

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Photo by alevision.co on Unsplash

Photo by alevision.co on Unsplash

Why is it important?

Analyzing transport timetables is an important task, as it brings the opportunity to discover which routes commonly lead to delays. We evaluated the proposed algorithm using Brazilian Flight Data and compared it with the direct application of frequent pattern mining approaches with and without functional dependencies. Our experiments indicate that our algorithm is about one order magnitude faster than other methods with functional dependencies.

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