Clustering analysis of railway driving missions with niching

Abstract

Purpose

A wide number of applications requires classifying or grouping data into a set of categories or clusters. The most popular clustering techniques to achieve this objective are K‐means clustering and hierarchical clustering. However, both of these methods necessitate the a priori setting of the cluster number. The purpose of this paper is to present a clustering method based on the use of a niching genetic algorithm to overcome this problem.

Design/methodology/approach

The proposed approach aims at finding the best compromise between the inter‐cluster distance maximization and the intra‐cluster distance minimization through the silhouette index optimization. It is capable of investigating in parallel multiple cluster configurations without requiring any assumption about the cluster number.

Findings

The effectiveness of the proposed approach is demonstrated on 2D benchmarks with non‐overlapping and overlapping clusters.

Originality/value

The proposed approach is also applied to the clustering analysis of railway driving profiles in the context of hybrid supply design. Such a method can help designers to identify different system configurations in compliance with the corresponding clusters: it may guide suppliers towards “market segmentation”, not only fulfilling economic constraints but also technical design objectives.

Citation

Jaafar, A., Sareni, B. and Roboam, X. (2012), "Clustering analysis of railway driving missions with niching", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 31 No. 3, pp. 920-931. https://doi.org/10.1108/03321641211209807