【正文】 soft Decision Trees algorithm provider, given each state of the predictable attribute, the algorithm calculates probabilities for each possible state of the input attribute. The algorithm provider processes the entire set of cases , iteratively paring the predicted classification of the cases with the known actual classification of the cases. The errors 6 from the initial classification of the first iteration of the entire set of cases is fed back into the work, and used to modify the work39。ve Bayes The Microsoft Na239。 畢業(yè)設(shè)計(jì) (論文 ) 外文文獻(xiàn)翻譯 專業(yè) 理學(xué)院 學(xué) 生 姓 名 李洪輝 班級(jí) 計(jì)科 092 學(xué)號(hào) 202101051 指 導(dǎo) 教 師 姚惠萍 1 英文原文 Introduction to Data Mining Abstract: Microsoft174。 SQL Server? 2021 provides an integrated environment for creating and working with data mining models. This tutorial uses four scenarios, targeted mailing, forecasting, market basket, and sequence clustering, to demonstrate how to use the mining model algorithms, mining model viewers, and data mining tools that are included in this release of SQL Server. Introduction The data mining tutorial is designed to walk you through the process of creating data mining models in Microsoft SQL Server 2021. The data mining algorithms and tools in SQL Server 2021 make it easy to build a prehensive 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. The most visible ponents in SQL Server 2021 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 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 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 exist in the data mining editor. Using the editor you can manage mining models, create new models, view models, pare 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 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 need to be able to determine which model is the most accurate. For this reason, the editor contains a model parison tool called the Mining Accuracy Chart tab. Using this tool you can pare the predictive accuracy of your models and determine the best model. To create predictions, you will use the Data Mining Extensions (DMX) language. DMX extends SQL, containing mands to create, modify, and predict against mining models. For 2 more information about DMX, see Data Mining Extensions (DMX) Reference in SQL Server Books Online. Because creating a prediction can be plicated, the data mining editor 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 which 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 tr