# Marketing Decision Support System Based on Data Mining Technology
## 3 Method
The tuple data of the training set are marked as \(D\) class, assuming that \(D\) class is marked with \(M\) different values, and the \(M\) different values represent \(M\) different classes, which are marked as \(C_{i}=(i=1,2\ldots m)\) in turn. A set of tuples in \(C_{i}\) class is denoted by \(C_{i}\), \(d\). \(|D|\) represents the number of data in a tuple. The expected information required in \(C_{i}\) is Formula (1):  
\[Info(D)=-{\sum_{i=1}^{m}{\log_{2}(Pi)}} \tag{1}\]  
\(Info(D)\) is the entropy of \(D\). \(P_{i}\) represents the probability that data belong to each tuple, and \(pi\) is calculated as \(|C_{i,D}|/|D|\). The tuple of data is supposed to be divided by \(A\), and \(A\) will obtain \(V\) different results after dividing the tuple, in which each division has a value \(aj\); thus, the information of \(A\) can be obtained, as shown in Formula (2):  
\[Info_{A}(D)=-{\sum_{j=1}^{v}{Info(D_{j})}} \tag{2}\]  
The information gain metric is used to select test attributes on each node of the tree. This metric is called attribute selection metric or split goodness metric. Select the attribute with the highest information gain or maximum entropy compression as the test attribute of the current node. This attribute minimizes the information and quantity required for sample classification in result division and reflects the minimum randomness of division. Assuming that the original information demand is \(Info(D)\) and the new information  
Figure 1: OLAP data organization model for DM sales analysis. demand is \(Info_{A}(D)\), the information gain is the difference between them, as shown in Formula (3):  
\[Gain(A)=Info(D)-Info_{A}(D) \tag{3}\]  
The information gain rate normalizes the information gain by using the split information \(\parallel\) value. Information classification is similar to \(Info(D)\), and is defined as Formula (4):  
\[SplitInfo_{A}(D)=-\sum\limits_{j=1}^{v}\log_{2}\frac{\left|D_{j}\right|}{\left| D\right|} \tag{4}\]  
This value represents the information generated by dividing the training data set \(D\) into \(v\) divisions corresponding to \(v\) outputs of the attribute \(A\) test. Select the attribute with the maximum gain rate as the split attribute, as shown in Formula (5):  
\[GainRation(A)=\frac{Gain(A)}{SplitInfo(A)} \tag{5}\]  
Let \(D_{k}\) be the distance from a sample to the \(K\) nearest neighbor. For a homogeneous Poisson process and \(x\in[0,\,\infty]\), there is Formula (6):  
\[P(D_{K}\geq x)=\sum\limits_{k=0}^{k-1}1-F_{D_{K}}(x) \tag{6}\]  
Then its distribution probability density is Formula (7):  
\[f_{D_{K}}(x)=\frac{dF_{D_{K}}(x)}{dx} \tag{7}\]  
The maximum likelihood estimation of parameter \(\lambda\) is Formula (8):  
\[\overset{\wedge}{\lambda}=\frac{K}{\pi\sum\limits_{i=1}^{n}d} \tag{8}\]  
where \(d\) is the \(K-NN\) distance of the \(i\) sample; \(n\) is the number of random samples. As mentioned above, the decision tree obtained through data set training can continuously subdivide the sales market according to the impact of various factors on sales in different levels of markets. (2) Architecture design of marketing DSSs  
The marketing decision support system designed in this paper can solve the problem of information asymmetry in the game with other companies and sales agents in sales. It adopts a message mechanism, so that managers can obtain the information of rival companies and the pre-determined information of the agent's team first, and can know all the sales in the next few days, so as to take the lead in the whole sales game. The business involved in the sales management system is relatively confidential. If it is directly placed on the public network, it is obviously not safe enough. Users on the public network using VPNs to connect to the company's intranet to access the sales management database and its applications is a better choice.