Clustering Based Bidding Price Ratio Recommendation Approach in Civil Engineering
Abstract
Bidding price ratio (BPR) is the ratio of the bidding price to the allocated budget, which its magnitude influences tender award probability and project execution risk. In view of this, this study performed statistical analysis on 66,932 tendered civil engineering projects in Taiwan from year 2010 to 2016. Crucial information fields were extracted and analyzed with data mining approaches, namely k-means and two-step data clustering methods. This is to investigate the bidding price ratio clustering characteristics of the tendered projects under different bidder amount. This study also implemented C5.0 method to evaluate influence factors among data clusters and their corresponding decision tree rules. Outcomes from this study may serve as references to the bidders during decision making of tender submission.
Keywords
Bidding price ratio, Data mining, K-means, Data clustering, Decision tree
DOI
10.12783/dtetr/ecar2018/26384
10.12783/dtetr/ecar2018/26384
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