Fuzzy clustering validity for contractor performance evaluation: Application to UAE contractors
Several statistical algorithms are used to categorize contractors. The number of categories depends on the clustering algorithm used. This paper presents a framework for classifying contractors using five of the most common clustering algorithms and assesses their performance with appropriate validi...
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| Vydané v: | Automation in construction Ročník 31; s. 158 - 168 |
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| Jazyk: | English |
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Elsevier B.V
01.05.2013
Elsevier |
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| ISSN: | 0926-5805 |
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| Abstract | Several statistical algorithms are used to categorize contractors. The number of categories depends on the clustering algorithm used. This paper presents a framework for classifying contractors using five of the most common clustering algorithms and assesses their performance with appropriate validity measures. The framework was implemented on actual data for 14 contractors working in UAE using a database of 294 projects. Quantitative measures were suggested and calculated for the contractors in the database. Qualitative measures were determined using AHP. The quality of contractor's staff and equipment was deemed to be the most important measure. The results show that contractors are grouped into four categories based on the quantitative and qualitative measures identified. The Fuzzy-C means algorithm had the highest validity measures when applied to the studied data set. The results show that the proposed framework can be used to categorize contractors into different performance groups in a rational and unbiased way.
► Framework for categorizing contractors using five clustering algorithms is introduced. ► Qualitative measures for assessing contractors' performance are identified. ► Different methods for categorizing contractors based on performance are introduced. |
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| AbstractList | Several statistical algorithms are used to categorize contractors. The number of categories depends on the clustering algorithm used. This paper presents a framework for classifying contractors using five of the most common clustering algorithms and assesses their performance with appropriate validity measures. The framework was implemented on actual data for 14 contractors working in UAE using a database of 294 projects. Quantitative measures were suggested and calculated for the contractors in the database. Qualitative measures were determined using AHP. The quality of contractor's staff and equipment was deemed to be the most important measure. The results show that contractors are grouped into four categories based on the quantitative and qualitative measures identified. The Fuzzy-C means algorithm had the highest validity measures when applied to the studied data set. The results show that the proposed framework can be used to categorize contractors into different performance groups in a rational and unbiased way.
► Framework for categorizing contractors using five clustering algorithms is introduced. ► Qualitative measures for assessing contractors' performance are identified. ► Different methods for categorizing contractors based on performance are introduced. Several statistical algorithms are used to categorize contractors. The number of categories depends on the clustering algorithm used. This paper presents a framework for classifying contractors using five of the most common clustering algorithms and assesses their performance with appropriate validity measures. The framework was implemented on actual data for 14 contractors working in UAE using a database of 294 projects. Quantitative measures were suggested and calculated for the contractors in the database. Qualitative measures were determined using AHP. The quality of contractor's staff and equipment was deemed to be the most important measure. The results show that contractors are grouped into four categories based on the quantitative and qualitative measures identified. The Fuzzy-C means algorithm had the highest validity measures when applied to the studied data set. The results show that the proposed framework can be used to categorize contractors into different performance groups in a rational and unbiased way. |
| Author | Hosny, Ossama Nassar, Khaled |
| Author_xml | – sequence: 1 givenname: Khaled surname: Nassar fullname: Nassar, Khaled email: knassar@aucegypt.edu – sequence: 2 givenname: Ossama surname: Hosny fullname: Hosny, Ossama email: ohosny@aucegypt.edu |
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| Cites_doi | 10.1061/(ASCE)9742-597X(1988)4:2(148) 10.1061/(ASCE)0733-9364(2000)126:5(331) 10.1061/(ASCE)0887-3801(1990)4:1(77) 10.1061/(ASCE)0742-597X(2002)18:2(52) 10.1061/(ASCE)0742-597X(2002)18:3(111) 10.1061/(ASCE)0733-9364(2004)130:5(691) 10.1080/01446190150505108 10.1016/S0360-1323(99)00069-4 10.1061/(ASCE)0733-9364(2006)132:9(998) 10.1016/S0926-5805(03)00020-7 10.1016/S0926-5805(00)00079-0 10.1016/0360-1323(94)90074-4 10.1061/(ASCE)0733-9364(2005)131:1(62) 10.1016/j.buildenv.2003.09.009 10.1061/(ASCE)0733-9364(2006)132:12(1242) 10.1061/(ASCE)0742-597X(1996)12:2(50) 10.1080/014461997373088 10.1061/(ASCE)0733-9364(2006)132:8(797) 10.1061/(ASCE)0733-9364(2008)134:3(179) 10.1061/(ASCE)0733-9364(2007)133:1(40) 10.1016/j.autcon.2006.09.005 10.1061/(ASCE)0733-9364(1985)111:3(231) 10.1016/0969-7012(94)90003-5 |
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| Keywords | Contractor performance Building construction Fuzzy clustering Categorization Cluster analysis Performance evaluation Building industry Building contractor Construction industry Fuzzy logic Qualitative analysis Application Deterministic approach Quantitative analysis |
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| References | Alarcón, Mourgues (bb0050) 2002; 18 Nassar (bb0060) 2009 Russell, Skibniewski (bb0080) 1998 Ng (bb0065) 2001; 10 Bubshait, Al-Gobali (bb0010) 1996; 12 Hatush, Skitmore (bb0020) 1997; 151 Russell, Skibniewski (bb0090) 1990; 4 Palaneeswaran, Kumaraswamy (bb9001) 2001; 36 Singh, Tiong (bb0105) 2005; 131 Sönmez, Holt, Yang, Graham (bb0115) 2002; 18 Abdelrahman, Zayed, Elyamany (bb0055) 2008; 134 Lam, Hu, Ng, Skitmore, Cheung (bb0045) 2000; 19 Palaneeswaran, Kumaraswamy (bb0075) 2000; 126 Wong (bb0125) 2004; 130 Elazouni (bb0135) 2006; 132 Li, Nie, Chen (bb0130) 2007; 133 Holt, Olomolaiye, Harris (bb0035) 1995; 1 Waara, Bröchner (bb0120) 2006; 132 Hatush, Skitmore (bb0025) 1998; 33 Nguyen (bb0070) 1985; 111 Shen, Lu, Shen, Li (bb0100) 2003; 122003 Holt, Olomolaiye, Harris (bb0030) 1994; 29 Ko, Cheng, Wu (bb0040) 2007; 16 Russell, Skibniewski (bb0085) 1998; 4 Topcu (bb9000) 2004; 39 Saaty (bb0095) 1980 Singh, Tiong (bb0015) 2006; 132 Balasko, Abonyi, Feil (bb0005) 2008 Abdelrahman (10.1016/j.autcon.2012.11.013_bb0055) 2008; 134 Ko (10.1016/j.autcon.2012.11.013_bb0040) 2007; 16 Ng (10.1016/j.autcon.2012.11.013_bb0065) 2001; 10 Balasko (10.1016/j.autcon.2012.11.013_bb0005) 2008 Hatush (10.1016/j.autcon.2012.11.013_bb0020) 1997; 151 Topcu (10.1016/j.autcon.2012.11.013_bb9000) 2004; 39 Wong (10.1016/j.autcon.2012.11.013_bb0125) 2004; 130 Palaneeswaran (10.1016/j.autcon.2012.11.013_bb0075) 2000; 126 Russell (10.1016/j.autcon.2012.11.013_bb0090) 1990; 4 Saaty (10.1016/j.autcon.2012.11.013_bb0095) 1980 Singh (10.1016/j.autcon.2012.11.013_bb0015) 2006; 132 Hatush (10.1016/j.autcon.2012.11.013_bb0025) 1998; 33 Li (10.1016/j.autcon.2012.11.013_bb0130) 2007; 133 Waara (10.1016/j.autcon.2012.11.013_bb0120) 2006; 132 Holt (10.1016/j.autcon.2012.11.013_bb0030) 1994; 29 Lam (10.1016/j.autcon.2012.11.013_bb0045) 2000; 19 Shen (10.1016/j.autcon.2012.11.013_bb0100) 2003; 122003 Sönmez (10.1016/j.autcon.2012.11.013_bb0115) 2002; 18 Holt (10.1016/j.autcon.2012.11.013_bb0035) 1995; 1 Nassar (10.1016/j.autcon.2012.11.013_bb0060) 2009 Nguyen (10.1016/j.autcon.2012.11.013_bb0070) 1985; 111 Alarcón (10.1016/j.autcon.2012.11.013_bb0050) 2002; 18 Russell (10.1016/j.autcon.2012.11.013_bb0085) 1998; 4 Elazouni (10.1016/j.autcon.2012.11.013_bb0135) 2006; 132 Bubshait (10.1016/j.autcon.2012.11.013_bb0010) 1996; 12 Singh (10.1016/j.autcon.2012.11.013_bb0105) 2005; 131 Palaneeswaran (10.1016/j.autcon.2012.11.013_bb9001) 2001; 36 Russell (10.1016/j.autcon.2012.11.013_bb0080) 1998 |
| References_xml | – year: 1980 ident: bb0095 article-title: The Analytic Hierarchy Process – volume: 12 start-page: 50 year: 1996 end-page: 54 ident: bb0010 article-title: Contractor prequalification in Saudi Arabia publication-title: Journal of Management in Engineering – year: 2009 ident: bb0060 article-title: Evaluating contractor performance: application to the Dubai construction industry publication-title: International Proceedings of the 45th Annual Conference, University of Florida, Gainesville, Florida, April 1–4, 2009 – volume: 18 start-page: 111 year: 2002 end-page: 119 ident: bb0115 article-title: Applying evidential reasoning to prequalifying construction contractors publication-title: Journal of Management in Engineering – volume: 134 start-page: 179 year: 2008 ident: bb0055 article-title: Best-value model based on project specific characteristics publication-title: Journal of Construction Engineering and Management – volume: 4 start-page: 77 year: 1990 end-page: 90 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start-page: 139 issue: 3 year: 1995 ident: 10.1016/j.autcon.2012.11.013_bb0035 article-title: Applying multiattribute analysis to contractor selection decisions publication-title: European Journal of Purchasing & Supply Management doi: 10.1016/0969-7012(94)90003-5 – year: 2008 ident: 10.1016/j.autcon.2012.11.013_bb0005 |
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| SubjectTerms | Algorithms Applied sciences Building construction Buildings. Public works Categories Categorization Clustering Contractor performance Contractors Exact sciences and technology Fuzzy clustering Fuzzy logic Mathematical analysis Miscellaneous United Arab Emirates |
| Title | Fuzzy clustering validity for contractor performance evaluation: Application to UAE contractors |
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