M. Sönmez, J.B. Yang, G.D. Holt, Addressing the contractor selection problem using an evidential reasoning approach, Engineering, Construction and Architectural Management, 8 (2001) 198-210.
 K.C. Lam, S. Thomas Ng, T. Hu, M. Skitmore, S.O. Cheung, Decision support system for contractor pre-qualification—artificial neural network model, Engineering, Construction and Architectural Management, 7 (2000) 251-266.
 D.J. Watt, B. Kayis, K. Willey, Identifying key factors in the evaluation of tenders for projects and services, International Journal of Project Management, 27 (2009) 250-260.
 G.D. Holt, Which contractor selection methodology?, International Journal of project management, 16 (1998) 153-164.
 J.B. Yang, W.C. Wang, Contractor selection by the most advantageous tendering approach in Taiwan, Journal of the Chinese Institute of Engineers, 26 (2003) 381-387.
 Z. Hatush, M. Skitmore, Assessment and evaluation of contractor data against client goals using PERT approach, Construction Management & Economics, 15 (1997) 327-340.
 S.T. Ng, R.M. Skitmore, Client and consultant perspectives of prequalification criteria, Building and environment, 34 (1999) 607-621.
 E. Palaneeswaran, M. Kumaraswamy, Recent advances and proposed improvements in contractor prequalification methodologies, Building and Environment, 36 (2001) 73-87.
 Y.I. Topcu, A decision model proposal for construction contractor selection in Turkey, Building and environment, 39 (2004) 469-481.
 M.K. J. Han, J. Pei, Data Mining concepts and techniques, (2012) 1-2.
 D. Pickell, Structured vs Unstructured Data – What's the Difference?, (2018).
 M. de Miranda Santo, G.M. Coelho, D.M. dos Santos, L. Fellows Filho, Text mining as a valuable tool in foresight exercises: A study on nanotechnology, Technological Forecasting and Social Change, 73 (2006) 1013-1027.
 N. Singh, C. Hu, W.S. Roehl, Text mining a decade of progress in hospitality human resource management research: Identifying emerging thematic development, International Journal of Hospitality Management, 26 (2007) 131-147.
 N.T. Ratrout, Subtractive clustering-based k-means technique for determining optimum time-of-day breakpoints, Journal of Computing in Civil Engineering, 25 (2010) 380-387.
 M. Al Qady, A. Kandil, Automatic clustering of construction project documents based on textual similarity, Automation in construction, 42 (2014) 36-49.
 R. Yan, Z. Ma, G. Kokogiannakis, Y. Zhao, A sensor fault detection strategy for air handling units using cluster analysis, Automation in Construction, 70 (2016) 77-88.
 H. Naganathan, W.O. Chong, X. Chen, Building energy modeling (BEM) using clustering algorithms and semi-supervised machine learning approaches, Automation in Construction, 72 (2016) 187-194.
 A.G. Kashani, A.J. Graettinger, Cluster-based roof covering damage detection in ground-based lidar data, Automation in Construction, 58 (2015) 19-27.
 E. Rodrigues, D. Sousa-Rodrigues, M.T. de Sampayo, A.R. Gaspar, Á. Gomes, C.H. Antunes, Clustering of architectural floor plans: A comparison of shape representations, Automation in Construction, 80 (2017) 48-65.
 S. Saitta, P. Kripakaran, B. Raphael, I.F. Smith, Improving system identification using clustering, Journal of Computing in Civil Engineering, 22 (2008) 292-302.
 I. Brilakis, L. Soibelman, Y. Shinagawa, Material-based construction site image retrieval, Journal of computing in civil engineering, 19 (2005) 341-355.
 S. Yarmohammadi, R. Pourabolghasem, D. Castro-Lacouture, Mining implicit 3D modeling patterns from unstructured temporal BIM log text data, Automation in Construction, 81 (2017) 17-24.
 Z. Ding, Z. Li, C. Fan, Building energy savings: Analysis of research trends based on text mining, Automation in Construction, 96 (2018) 398-410.