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

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

Al-Douri, Yamur K. (author): Luleå tekniska universitet,Drift, underhåll och akustik

Hamodi, Hussan (author): Luleå tekniska universitet,Drift, underhåll och akustik

Zhang, Liangwei (author): Department of Industrial Engineering, School of Mechanical Engineering, Dongguan University of Technology, 523808 Dongguan, China

(creator_code:org_t)

2018-09-11
2018
English.
In: Cogent Engineering. - : Taylor & Francis. - 2331-1916. ; 5:1, s. 1-16

Related links:: https://doi.org/10.1...; show more...; https://doi.org/10.1...; https://urn.kb.se/re...; https://doi.org/10.1...; show less...

Journal article (peer-reviewed)

Abstract Subject headings

Data clustering captures natural structures in data consisting of a set of objects and groups similar data together. The derived clusters can be used for scale analysis and to posit missing data values in objects, as missing data have a negative effect on the computational validity of models. This study develops a new two-level multi-objective genetic algorithm (GA) to optimize clustering in order to redact and impute missing cost data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). The first level uses a multi-objective GA based on fuzzy c-means to cluster cost data objects based on three main indices. The first is cluster centre outliers; the second is the compactness and separation ( ) of the data points and cluster centres; the third is the intensity of data points belonging to the derived clusters. Our clustering model is validated using k-means clustering. The second level uses a multi-objective GA to impute the missing cost redacted data in size using a valid data period. The optimal population has a low , 0.1%, and a high intensity, 99%. It has three cluster centres, with the highest data reduction of 27%. These three cluster centres have a suitable geometry, so the cost data can be partitioned into relevant contents to be redacted for imputing. Our model show better clustering detection and evaluation compared with k-means. The amount of missing data for the two cost objects are: labour 57%, materials 81%. The second level shows highly correlated data (R-squared 0.99) after imputing the missing data objects. Therefore, multi-objective GA can cluster and impute data to derive complete data that can be used for better estimation of forecasting.

Subject headings

TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Tillförlitlighets- och kvalitetsteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Reliability and Maintenance (hsv//eng)
TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Annan samhällsbyggnadsteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Other Civil Engineering (hsv//eng)

Keyword

Data clustering
data imputing
multi-objective GA
fuzzy c-means
K-means clustering
Operation and Maintenance Engineering
Drift och underhållsteknik

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ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Mechanical Engin ...; and Reliability and ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Civil Engineerin ...; and Other Civil Engi ...

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By the university: Luleå University of Technology

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