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.

Reliability analysis in railway repairable systems

“…The creation of clusters in repairable systems through the selection of variables of notable items, simplifies decision making when the number of systems is very high. For examples and procedures see the following works: Juarez et al (2011); variables “dynamic parameters” applied to connection of multi-area power systems. Yu and Chan (2012); variables “temperatures, power and flow” applied to operating performance of chiller systems. Jaafar et al (2012); variable “driving profiles” applied to the design of railway locomotives. Shang and Wang (2015); variables “reliability, economy, and operational” applied to the power generation group. Rastegari and Mobin (2016); variables “cost, frequency and downtime” applied to maintenance management system.…”

Recommendation of Collaborative Activities in E-learning Environments

“…For this reason, the proposed solution uses dynamic clustering [6], which classifies learners on the basis of similar learning needs and interests, without requiring an initial indication of the number of clusters. In particular, the proposed approach uses the Silhouette index [5] to estimate the optimal number of clusters in which to group the data set and the K-means algorithm [4] to cluster the data set into the optimal, previously defined partition.…”

Multilocal Search and Adaptive Niching Based Memetic Algorithm With a Consensus Criterion for Data Clustering

“…In [17], a variant of the standard crowding technique was also incorporated into the GA to evolve cluster centers of web documents. Jaafar et al [43] employed the RTS approach to niching in GA while performing clustering of the railway driving missions. These algorithms have all employed niching techniques with the purpose of preserving population diversity during the evolutionary clustering process.…”