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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
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.ORCID-id: 0000-0002-1967-6604
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.ORCID-id: 0000-0001-5620-5265
Department of Industrial Engineering, School of Mechanical Engineering, Dongguan University of Technology, 523808 Dongguan, China.ORCID-id: 0000-0001-5317-0087
2018 (Engelska)Ingår i: Cogent Engineering, ISSN 2331-1916, Vol. 5, nr 1, s. 1-16, artikel-id 1513304Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
Taylor & Francis, 2018. Vol. 5, nr 1, s. 1-16, artikel-id 1513304
Nationell ämneskategori
Tillförlitlighets- och kvalitetsteknik Annan samhällsbyggnadsteknik
Forskningsämne
Drift och underhållsteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-70375DOI: 10.1080/23311916.2018.1513304ISI: 000444436800001OAI: oai:DiVA.org:ltu-70375DiVA, id: diva2:1238830
Anmärkning

Validerad;2018;Nivå 2;2018-10-08 (johcin) 

Tillgänglig från: 2018-08-14 Skapad: 2018-08-14 Senast uppdaterad: 2018-10-08Bibliografiskt granskad

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Al-Douri, Yamur K.Hamodi, Hussan

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Al-Douri, Yamur K.Hamodi, HussanZhang, Liangwei
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