Research On Cost Management Method Based On Data Mining

Cost management is an important research content in management field. At present, cost management methods almost adopt character contents to describe some solving strategies, and use basic formulas to analyze some business problems from the perspective of financial management method. In that way, it lacks the advanced intelligent quantitative method. Meanwhile, with the explosive growth of data in the information society, government, enterprises and other institutions have accumulated vast amounts of cost data. In the new era of knowledge economy, how to transfer "data ocean" to "knowledge gold mine" and let cost management develop towards the digital, intelligent and knowledgeable direction, information technology, especially the emergence and development of data mining technology, can provide strong support for that requirements.In view of the integration issues between data mining and cost management, on the basis of analyzing the domestic and overseas research and application status of data mining technology, cost management theory and its combined method, as well as under the background of practice and application in Dongbei Special Steel Group Co. Ltd., a number of key technologies of cost management method based on data mining are studied by this paper through using multi-disciplinary knowledge and cross-application integration.Firstly, the paper studies the composing element model of cost management based on data mining, and introduces the model's data mining object, cost data source, costing method and composing elements. Then how to implement the enterprises'data mining tasks for completing the solution programs of the specific business problem in the model is explained in detail. Secondly, the paper adopts an improved algorithm of association rule mining to accomplish the activity selection and mergence tasks of activity-based costing, which can efficiently realize the critical activity selection and critical activity mergence under the condition of large-scale activity data. Then, a cost prediction method based on an optimized fuzzy model is presented. It can not only realize the higher prediction accuracy for time-series data of product costs, but also make the prediction process resemble human reasoning process. It also improves the applicability of cost prediciton method. A factor identification method of cost decision based on an improved rough set reduction is also proposed, which can generate the minimization rules in the consistent cost decision table. It also get simplified and optimized cost decision factors in the effective decision rules. The paper also puts forward a cost rank analysis method based on an optimized dynamic fuzzy clustering algorithm. In each accounting period, it can automatically classify the product costs into the affiliated rank depending upon the structure of data themselves and the underlying complexity of data dynamics, which effectively realizes dynamic cost rank analysis. Moreover, an exceptional cost control method based on an outlier detection algorithm is also proposed. It can find out the exceptional product costs and its significant deviation dimension in the environment of multi-dimensional cost data, which clearly identifies the direction of exceptional cost control. Finally, by combining the thesis research and real-world practice, and following the case enterprise's business requirements of cost management, application background as well as performance data, the results have demonstrated the effectiveness and practicability of the cost management method based on data mining. The research fruits can be cited and referred by other industries and enterprises for achieving their intelligent and knowledge-based cost management.