【文章內(nèi)容簡介】 se tools can be used to perform some of the most important tasks in data mining: cleaning and preparing the data for model creation. In data mining, you typically perform 4 repetitive data transformations to clean the data before using the data to train a mining model. Using the tasks and transformations in DTS, you can bine data preparation and model creation into a single DTS package. DTS also provides DTS Designer to help you easily build and run packages containing all of the tasks and transformations. Using DTS Designer, you can deploy the packages to a server and run them on a regularly scheduled basis. This is useful if, for example, you collect data weekly data and want to perform the same cleaning transformations each time in an automated fashion. You can work with a Data Transformation project and an Analysis Services project together as part of a business intelligence solution, by adding each project to a solution in Business Intelligence Development Studio. Mining Model Algorithms Data mining algorithms are the foundation from which mining models are created. The variety of algorithms included in SQL Server 2021 allows you to perform many types of analysis. For more specific information about the algorithms and how they can be adjusted using parameters, see Data Mining Algorithms in SQL Server Books Online. Microsoft Decision Trees The Microsoft Decision Trees algorithm supports both classification and regression and it works well for predictive modeling. Using the algorithm, you can predict both discrete and continuous attributes. In building a model, the algorithm examines how each input attribute in the dataset affects the result of the predicted attribute, and then it uses the input attributes with the strongest relationship to create a series of splits, called nodes. As new nodes are added to the model, a tree structure begins to form. The top node of the tree describes the breakdown of the predicted attribute over the overall population. Each additional node is created based on the distribution of states of the predicted attribute as pared to the input attributes. If an input attribute is seen to cause the predicted attribute to favor one state over another, a new node is added to the model. The model continues to grow until none of the remaining attributes create a split that provides an improved prediction over the existing node. The model seeks to find a bination of attributes and their states that creates a disproportionate distribution of states in the predicted attribute, therefore allowing you to predict the oute of the predicted attribute. Microsoft Clustering The Microsoft Clustering algorithm uses iterative techniques to group records from a dataset into clusters containing similar characteristics. Using these clusters, you can explore the data, learning more about the relationships that exist, which may not be easy to derive logically through casual observation. Additionally, you can create predictions from the clustering model created by 5 the algorithm. For example, consider a group of people who live in the same neighborhood, drive the same kind of car, eat the same kind of food, and buy a similar version of a product. This is a cluster of data. Another cluster may include people who go to the same restaurants, have similar salaries, and vacation twice a year outside the country. Observing how these clusters are distributed, you can better understand how the records in a dataset interact, as well as how that interaction affects the oute of a predicted attribute. Microsoft Na239。ve Bayes The Microsoft Na239。ve Bayes algorithm quickly builds mining models that can be used for classification and prediction. It calculates probabilities for each possible state of the input attribute, given each state of the predictable attribute, which can later be used to predict an oute of the predicted attribute based on the known input attributes. The probabilities used to generate the model are calculated and stored during the processing of the cube. The algorithm suppor