| With the development of strategy of information drives industrialization in China, the construction of information has become the main approach to enhance core competence for enterprises.And enterprise development is closely related to the IT project evaluation and selection is more and more the concern of many enterprises. Choose a suitable IT project developers or IT project products are generally concerned businesses and the urgent need to solve a problem.IT projects bidding evaluation contains several characteristics as below:the great money involved,the many indicators, the close level among the bidders,the great relative degree,the low transparency etc, with the high un-structural feature,which leads to a complicated process of decision making and the difficulty to solve the problem in a single evaluation way.However, traditional project risk analysis and decision-making are based on managers' experience and assumed many conditions,thus there are great differences between the result of analysis and actual situation.Thereby the traditional method has certain subjectivity and randomness.Aiming at these problems,theoretical analysis,positivism,qualitative analysis and quantitative analysis are the fundamental research methodology of this dissertation together with the support from the disciplines such as project management,decision science,system science and information science.The system of indicators in IT projects bidding evaluation is established,and the method is given to distribute the weight of indicator.This paper have completed an evaluation decision model which is established based on rough set and grey clustering theory through the characteristics of IT projects and bidding for IT projects.The principle is to make use of the way of calculating the importance of each attribute to get the weight of each indicator of the first class,then to evaluate comprehensively the scores given by experts based on grey clustering theory. Through implied the rule mining method combined the Rough set and Bayesian theorem to IT project risk decision,the Rough set-Bayesian classifiers not only reduce the size of data,also obtain the ability of classifying incomplete data and learning new knowledge with increment.So on one hand,it avoids the disadvantage of the Bayesian classifiers being dependant upon experience overly,on the other hand,it overcomes the shortcoming of using Rough set completely to deduce rules.It indicates that,integrative rule mining system that is constructed by combining different methods can overcome individual's limitation and the function is more powerful than the single system. |
