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Application of machine learning methods for risk analysis of unfavorable outcome of government procurement procedure in building and grounds maintenance domain

机译：机器学习方法在建设和理由维护领域的政府采购程序不利结果风险分析中的应用

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The article provides the results of applying machine learning methods for prediction of unfavorable outcome of the public procurement procedure in the building and grounds maintenance domain. Based on a comprehensive analysis of the domain it was decided to investigate the following risks: the risk of collusion among suppliers; the risk of conspiracy between customers and suppliers; the risk associated with inaccurate data in the Unified Information System. Usage of various classification techniques has been researched while modeling the problem in the domain. In order to form sustainable groups of suppliers, the association rule mining was done using the "Apriori" algorithm. While searching for representative characteristics of the groups of similar objects, the solution to the clustering problem was found using the Ward and K-means++ methods. The Cluster models, which were defined to analyze each of the collusion risks, were built on the feature space. The models make it possible to identify the most typical behavioral patterns of two suppliers with each other as well as the customer with the supplier.
机译：本文提供了应用机器学习方法的结果，以便预测建筑物和地面维护领域的公共采购程序的不利结果。根据对域的全面分析，决定调查以下风险：供应商之间的勾结风险;客户和供应商之间阴谋的风险;统一信息系统中数据相关的风险。在建模域中的问题的同时研究了各种分类技术的使用。为了形成可持续的供应商组，使用“APRiori”算法进行关联规则挖掘。在寻找类似对象组的代表性特征的同时，使用Ward和K-Means ++方法找到聚类问题的解决方案。被定义为分析每个勾结风险的群集模型是基于特征空间构建的。该模型可以互相识别两个供应商的最典型的行为模式以及供应商的客户。

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