Based On Financial Analysis Of The Data Mining Research And Application

The unceasing boost of Enterprise Information-based Implementation has accumulated a huge business data source for many companies. But we have not made the best use of the commercial value of this data source until now. The traditional financial analysis only works on the limited finance date by simply applying statistics, which can hardly works on the thousands of industry data sources, nor deeply understand the underlying and deep-seated information behind the numerous data. Among all the information tools, the Data Mining Tool, to be one of the most exquisite, efficient and complicated tools, outstands in catching the useful information from the huge data resource to analyze the unique business rules behind it, which is very helpful to shape decisions scientifically and to forecast the business trends. This paper focuses on the study of how to bring out this advantage of Data Mining Tool and to combine it with financial analysis in application.This paper describes the application of data mining theory and method in financial analyzing. It presents the way how to closely integrate the technology of data mining with financial analysis against the background of Business Intelligent Application, and how to build an analysis modeling of finance that adopts methods of clustering, affiliated rules and decision tree in joint analysis, ground on the online data of financial statement from steel industry, which we found can significantly improve the efficiency of financial analyzing. This research has a high value in use.Following three aspects mostly show us the creative spirit of this paper:1. The data pre-processing can greatly improves the efficiency of association analysis: Map the irregular data into a Boolean matrix through data processing, thus the association analysis transfers from data mining to study on that Boolean matrix. This change helps to avoid the I/O block and to improve the performance of memory which are usually caused by the overfrequent visit the database, or by the mass of candidate itemsets.2. Application of financial analysis modeling that adopts methods of clustering, affiliated rules and decision tree in joint analysis. This modeling assists us to obtain useful information from the financial statement more effectively and correctly.3. To deduct from the rules of ROI (Return on Investment), you'll get an improved ROI rule which can be used in the financial analyse.