## Materials and Methods  
### Literature Review  
Numerous studies have been conducted on bidding model for construction projects. Such research can be divided into the study of model development to support bid decision making [4; 5; 10; 11; 12; 13; 14] and study to determine bid cost and contingencies in consideration of bidding risks [8; 15; 16].  
The previous studies related to the bid decision model for construction projects show the factors that affect the bid price. In [10], a quantitative analysis of the impact of the number of project bidders on project bid price was presented. The study found that reducing the number of bidders will result in increased project bid prices. In [4], a Case-based reasoning (CBR) approach in bid decision making was suggested using internal biding factors (contractor's expertise, experience, financial ability, resource, etc.) and external biding factors (nature of the work, biding requirement, social, and economic environment). A bid decision-making model based on the fuzzy set theory was proposed in [12]. In that study, the most vital factors influencing the bid/no bid decision are the type of work, experience in similar projects, and contractual terms.  
The way in which risk is accounted for in the bidding process was studied in [16]. The types of uncertainty that occur in the bidding document of construction projects were defined in [8], and the factors that cause cost overruns were identified. Based on the results, the authors of [8] proposed a bid contingency cost that can be prepared for uncertain risks.  
Previous studies related to bid price forecasting and bid decision models reveal that many factors affect the bid price and bid decisions, and the project's characteristics appear to be one of the most important influential factors among many bid risk factors. While many studies have presented quantitative research results for bidding decision, the analysis of the factors that can specify the characteristics of the project (that is, the factors hidden in the bidding documents) were not usually considered. In other words, even though the bidding document contains information that describes the characteristics of the project, studies on bidding risk prediction based on detailed analysis of the bidding document have not been conducted. Most of the previous studies have analyzed data using structured numerical data, and most of these numerical data are obtained from questionnaires completed by related field experts. While the method of obtaining quantitative figures of qualitative factors through surveys is effective, this data can often be inaccurate and unreliable.  
In [17], cost overrun was predicted by integrating the text data and numeric data of a construction bidding document; however, the study has a limitation since the authors did not fully consider the many potential uncertainty risks that are included in the bidding document and used only short text summary data. In addition, the prediction model has an accuracy of less than 50%, which may reduce the reliability of the analysis results.  
This study considers that most of the important information that determines a project's characteristics is in the form of unstructured text data; an integrated data analysis approach using unstructured text data and numeric data is therefore proposed. In this study, we analyze the text of pre-bid clarification information in order to examine bidding document in which the characteristics of projects and risks exist, in order to identify the type of information that is not clear in the bidding document. Finally, the bidding risks of construction projects are predicted by integrating the unstructured text data with the structured numeric data. Furthermore, this study proves the effectiveness of integrated data mining by comparing the predicted results from the integrated data analysis proposed in this study and the results from only numeric data analysis.