Build, Based On Unit Outlier Algorithm And Customer Loyalty Analysis System

Data Mining is a new technique developed from 1980s.It aims to extract the implicit, previously unknown, and potentially useful knowledge from voluminous, non-complete, fuzzy, stochastic data. Outliers 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. After 20 years of development, on the theory, data mining techniques is becoming more and more consummate and is expanding its application area. Now, data mining has been used in telecom, finance, busyness, weather forecast, DNA, stock market, intrusion detection and customer segmentation etc. So in this paper we first research the algorithm of outlier detection based cell, point out and improve on its shortcomings, then design a system ofcustomer loyalty analysis to settle the customer loyalty analysis problem based this algorithm and some other data mining techniques, final, analyze customer loyalty of Haier company based its customer relationship data.First, we described the background of research and pointed out its significance. The domestic and foreign situation of data mining research was analyzed from theoretical and applying aspects. After analyzing the general progress of knowledge discovery we gave a classic framework of a data mining system, analyzed main function of every module and expatiate on the technique of data mining.Second, 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 these algorithms are compared.Third, Based on the algorithm of based-cell outlier detection, an outlier-analysis algorithm to reduce the boundary influence is presented. The data spatial cell partitioning and data object allocating methods based on the problem of boundary outlier misjudgment in cell outlier mining algorithms are discussed. Then a dynamic adjustment function on dataset boundary threshold is defined and an improved algorithm on the cell-based outlier is brought forward. It can greatly reduce the amount of misjudgment on boundary outlier by the algorithm discussed in this paper without increasing the complexity and the calculating time of the original algorithm. The validity of the new algorithm has been verified by some instances. Finally, we used this algorithm in the edge extraction of colorface images and the effect is satisfying.Forth, a customer loyalty analysis system is designed. The definition of customer loyalty is present. The significance to research the customer loyalty is indicated. Then the functions of this system are explained, which include data preprocessing, key customer finding, customer loyalty partitioning. The preprocessing methods of data preprocessing module are discuss. Data mining techniques used in key customer finding module and customer loyalty partitioning module are given out. The algorithms, which we use in these techniques, are depicted. Finally, result visualization module is introduced) which include tow method: parallel coordinates and categorical chart.Fifth, the customer loyalty of Haier Company is analyzed by the customer loyalty analysis system. In order to analyze the customer loyalty of Haier Company, the process and way, which we choose and deal the analyzed data, is discussed. The results of the key customer finding is analyzed and compared based different parameters. The rule of parameter change and key customer finding is got. Beside these, the classes of Haier customer data are educed by clustering. Then, the proper data objects are chosen as the train set. The final loyalty classes of customer relationship data are got by the prediction algorithm of neural network. The validity of customer loyalty analysis system is verified.Finally, all the results are summarized, and the study prospect is discussed.