Data clustering and imputing using a two-level multi-objective genetic algorithm (GA): A case study of maintenance cost data for tunnel fans

Data clustering and imputing using a two-level multi-objective genetic algorithm (GA): A case study of maintenance cost data for tunnel fans

This study develops a new two-level multi-objective genetic algorithm (GA) to optimise clustering to reduce and impute missing cost data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 uses a multi-objective GA based on fuzzy c-means to cluster cost data...

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Bibliographic Details
Main Authors: Yamur K. Aldouri, Hassan Al-Chalabi, Liangwei Zhang
Format: Article
Language:English
Published: Taylor & Francis Group 2018-01-01
Series:Cogent Engineering
Subjects:
data clustering
data imputing
multi-objective ga
fuzzy c-means
k-means clustering
Online Access:http://dx.doi.org/10.1080/23311916.2018.1513304

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