Online from: 1982
Subject Area: Electrical & Electronic Engineering
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|Title:||Clustering analysis of railway driving missions with niching|
|Author(s):||Amine Jaafar, (LAPLACE UMR CNRS-INPT-UPS, Université de Toulouse, Toulouse, France), Bruno Sareni, (LAPLACE UMR CNRS-INPT-UPS, Université de Toulouse, Toulouse, France), Xavier Roboam, (LAPLACE UMR CNRS-INPT-UPS, Université de Toulouse, Toulouse, France)|
|Citation:||Amine Jaafar, Bruno Sareni, Xavier Roboam, (2012) "Clustering analysis of railway driving missions with niching", COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol. 31 Iss: 3, pp.920 - 931|
|Keywords:||Cluster analysis, Clustering, Data management, Driving missions, Genetic algorithms, K-means, Niching genetic algorithms, Railway locomotive, Silhouette index|
|Article type:||Research paper|
|DOI:||10.1108/03321641211209807 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
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.
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