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1、精选优质文档-倾情为你奉上毕业设计(论文)外文文献翻译专业理学院学生姓名李洪辉班级计科092学号指导教师姚惠萍英文原文Introduction to Data MiningAbstract: Microsoft SQL Server 2005 provides an integrated environment for creating and working with data mining models. Thistutorial uses four scenarios, targetedmailing,forecasting,marketbasket, andsequencecluste

2、ring, to demonstrate how to use the mining model algorithms, mining model viewers, and data mining toolsthat are included in this release of SQL Server.IntroductionThe data mining tutorial is designed to walk you through the process of creating data mining models in Microsoft SQL Server 2005. The da

3、ta mining algorithms and tools in SQL Server 2005 make it easy to build a comprehensive solution for a variety of projects, including market basket analysis, forecasting analysis, and targeted mailing analysis. The scenarios for these solutions are explained in greater detail later in the tutorial.

4、The most visible components in SQL Server 2005 are the workspaces that you use to create and work with data mining models. The online analytical processing (OLAP) and data mining tools are consolidated into two working environments: Business Intelligence Development Studio and SQL Server Management

5、Studio. Using Business Intelligence Development Studio, you can develop an Analysis Services project disconnected from the server. When the project is ready, you can deploy it to the server. You can also work directly against the server. The main function of SQL Server Management Studio is to manage

6、 the server. Each environment is described in more detail later in this introduction. For more information on choosing between the two environments, see Choosing Between SQL Server Management Studio and Business Intelligence Development Studio in SQL Server Books Online.All of the data mining tools

7、exist in the data mining editor. Using the editor you can manage mining models, create new models, view models, compare models, and create predictions based on existing models. After you build a mining model, you will want to explore it, looking for interesting patterns and rules. Each mining model

8、viewer in the editor is customized to explore models built with a specific algorithm. For more information about the viewers, see Viewing a Data Mining Model in SQL Server Books Online.Often your project will contain several mining models, so before you can use a model to create predictions, you nee

9、d to be able to determine which model is the most accurate. For this reason, the editor contains a model comparison tool called the Mining Accuracy Chart tab. Using this tool you can compare the predictive accuracy of your models and determine the best model. To create predictions, you will use the

10、Data Mining Extensions (DMX) language. DMX extends SQL, containing commands to create, modify, and predict against mining models. For more information about DMX, see Data Mining Extensions (DMX) Reference in SQL Server Books Online. Because creating a prediction can be complicated, the data mining e

11、ditor contains a tool called Prediction Query Builder, which allows you to build queries using a graphical interface. You can also view the DMX code that is generated by the query builder. Just as important as the tools that you use to work with and create data mining models are the mechanics by whi

12、ch they are created. The key to creating a mining model is the data mining algorithm. The algorithm finds patterns in the data that you pass it, and it translates them into a mining model it is the engine behind the process. Some of the most important steps in creating a data mining solution are con

13、solidating, cleaning, and preparing the data to be used to create the mining models. SQL Server 2005 includes the Data Transformation Services (DTS) working environment, which contains tools that you can use to clean, validate, and prepare your data. For more information on using DTS in conjunction

14、with a data mining solution, see DTS Data Mining Tasks and Transformations in SQL Server Books Online.In order to demonstrate the SQL Server data mining features, this tutorial uses a new sample database called AdventureWorksDW. The database is included with SQL Server 2005, and it supports OLAP and

15、 data mining functionality. In order to make the sample database available, you need to select the sample database at the installation time in the “Advanced” dialog for component selection.Adventure WorksAdventureWorksDW is based on a fictional bicycle manufacturing company named Adventure Works Cyc

16、les. Adventure Works produces and distributes metal and composite bicycles to North American, European, and Asian commercial markets. The base of operations is located in Bothell, Washington with 500 employees, and several regional sales teams are located throughout their market base. Adventure Work

17、s sells products wholesale to specialty shops and to individuals through the Internet. For the data mining exercises, you will work with the AdventureWorksDW Internet sales tables, which contain realistic patterns that work well for data mining exercises. For more information on Adventure Works Cycl

18、es see Sample Databases and Business Scenarios in SQL Server Books Online.Database DetailsThe Internet sales schema contains information about 9,242 customers. These customers live in six countries, which are combined into three regions:North America (83%)Europe (12%)Australia (7%)The database conta

19、ins data for three fiscal years: 2002, 2003, and 2004. The products in the database are broken down by subcategory, model, and product.Business Intelligence Development StudioBusiness Intelligence Development Studio is a set of tools designed for creating business intelligence projects. Because Busi

20、ness Intelligence Development Studio was created as an IDE environment in which you can create a complete solution, you work disconnected from the server. You can change your data mining objects as much as you want, but the changes are not reflected on the server until after you deploy the project.W

21、orking in an IDE is beneficial for the following reasons:The Analysis Services project is the entry point for a business intelligence solution. An Analysis Services project encapsulates mining models and OLAP cubes, along with supplemental objects that make up the Analysis Services database. From Bu

22、siness Intelligence Development Studio, you can create and edit Analysis Services objects within a project and deploy the project to the appropriate Analysis Services server or servers.If you are working with an existing Analysis Services project, you can also use Business Intelligence Development S

23、tudio to work connected the server. In this way, changes are reflected directly on the server without having to deploy the solution.SQL Server Management StudioSQL Server Management Studio is a collection of administrative and scripting tools for working with Microsoft SQL Server components. This wo

24、rkspace differs from Business Intelligence Development Studio in that you are working in a connected environment where actions are propagated to the server as soon as you save your work. After the data has been cleaned and prepared for data mining, most of the tasks associated with creating a data m

25、ining solution are performed within Business Intelligence Development Studio. Using the Business Intelligence Development Studio tools, you develop and test the data mining solution, using an iterative process to determine which models work best for a given situation. When the developer is satisfied

26、 with the solution, it is deployed to an Analysis Services server. From this point, the focus shifts from development to maintenance and use, and thus SQL Server Management Studio. Using SQL Server Management Studio, you can administer your database and perform some of the same functions as in Busin

27、ess Intelligence Development Studio, such as viewing, and creating predictions from mining models. Data Transformation ServicesData Transformation Services (DTS) comprises the Extract, Transform, and Load (ETL) tools in SQL Server 2005. These tools can be used to perform some of the most important t

28、asks in data mining: cleaning and preparing the data for model creation. In data mining, you typically perform 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 combine data preparation and model creat

29、ion 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

30、 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 Intelligen

31、ce Development Studio.Mining Model AlgorithmsData mining algorithms are the foundation from which mining models are created. The variety of algorithms included in SQL Server 2005 allows you to perform many types of analysis. For more specific information about the algorithms and how they can be adju

32、sted using parameters, see Data Mining Algorithms in SQL Server Books Online.Microsoft Decision TreesThe 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 attribu

33、tes. 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 struct

34、ure 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 compared to the input attributes. If an input attribute is seen to cause the

35、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 combination of attributes and their states t

36、hat creates a disproportionate distribution of states in the predicted attribute, therefore allowing you to predict the outcome of the predicted attribute.Microsoft ClusteringThe Microsoft Clustering algorithm uses iterative techniques to group records from a dataset into clusters containing similar

37、 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 the algorithm. For example, consider

38、 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

39、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 outcome of a predicted attribute.Microsoft Nave BayesThe Microsoft Nave Bayes algorithm quickly builds mining models that can be use

40、d 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 outcome of the predicted attribute based on the known input attributes. The probabilities used to gene

41、rate the model are calculated and stored during the processing of the cube. The algorithm supports only discrete or discretized attributes, and it considers all input attributes to be independent. The Microsoft Nave Bayes algorithm produces a simple mining model that can be considered a starting poi

42、nt in the data mining process. Because most of the calculations used in creating the model are generated during cube processing, results are returned quickly. This makes the model a good option for exploring the data and for discovering how various input attributes are distributed in the different s

43、tates of the predicted attribute.Microsoft Time SeriesThe Microsoft Time Series algorithm creates models that can be used to predict continuous variables over time from both OLAP and relational data sources. For example, you can use the Microsoft Time Series algorithm to predict sales and profits ba

44、sed on the historical data in a cube.Using the algorithm, you can choose one or more variables to predict, but they must be continuous. You can have only one case series for each model. The case series identifies the location in a series, such as the date when looking at sales over a length of sever

45、al months or years. A case may contain a set of variables (for example, sales at different stores). The Microsoft Time Series algorithm can use cross-variable correlations in its predictions. For example, prior sales at one store may be useful in predicting current sales at another store.Microsoft N

46、eural NetworkIn Microsoft SQL Server 2005 Analysis Services, the Microsoft Neural Network algorithm creates classification and regression mining models by constructing a multilayer perceptron network of neurons. Similar to the Microsoft Decision Trees algorithm provider, given each state of the pred

47、ictable attribute, the algorithm calculates probabilities for each possible state of the input attribute. The algorithm provider processes the entire set of cases , iteratively comparing the predicted classification of the cases with the known actual classification of the cases. The errors from the

48、initial classification of the first iteration of the entire set of cases is fed back into the network, and used to modify the networks performance for the next iteration, and so on. You can later use these probabilities to predict an outcome of the predicted attribute, based on the input attributes.

49、 One of the primary differences between this algorithm and the Microsoft Decision Trees algorithm, however, is that its learning process is to optimize network parameters toward minimizing the error while the Microsoft Decision Trees algorithm splits rules in order to maximize information gain. The algorithm supports the prediction of both discrete and continuous attributes.Microsoft Linear RegressionT