Abnormal Data Mining Algorithm And Its Application Of The Tax

In this article we will apply clustering and outlier detection method on data of tax.As the database technology has been used on revenue widely, revenue has accumulated a large number of row data, which are saved in Database to little avail. How to abstract knowledge from these data is the key task of Data mining technology. Data Mining is a new technique developed from 1980s. It aims to extract the implicit, unknown, and potentially useful knowledge from voluminous, non-complete, fuzzy, stochastic data.Outlier analysis is a important part of data mining research. Its purpose is to find the "small patterns" from dataset. An outlier is an object that is considerably dissimilar or inconsistent with the remainder of the data. This is very useful in revenue. The outlier in revenue database could be generated by a special mode of production, a large-scale taxpayer, or even criminality. All of these are in special supervision of revenue. It is important for revenue to find them quickly and accurately. The outlier detection technology adapted to revenue is discussed in this article.Firstly, we describe the basic concepts and method. Then introduce the commonly objects and representative applications. We study clustering and outlier detection technology and describe the commonly rules, and introduce some clustering and outlier detection algorithms. The research process and the current situation of outlier detection are reviewed. The algorithms of outlier detection based distance; density, deviation and high dimension are introduced. The content of these algorithms is analyzed. The disadvantages and advantages of thesealgorithms are compared.The emphasis of this article is using ODACDS(outlier detection algorithm on Continued Data Sets), one of density-based clustering method to analyze the data of tax. The algorithm can discover arbitrary shape clusters and can distinguish noise. Owing to the feature of tax, outlier detection can be used widely in the field. For the demand of revenue, we studied all kinds of algorithms about outlier detection and Clustering. On the base of studying the clustering algorithm based outlier factor, we bring forward an outlier detection algorithm on Continued Data Sets. The new algorithms can be use to find the excursion of the data. We firstly introduce the concepts of outlier factor, then explain the idea and process of the algorithm, and do some discuss for the detail and exception.