外文文獻(xiàn)翻譯---數(shù)據(jù)挖掘技術(shù)簡介-其他專業(yè)-資料下載頁
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【導(dǎo)讀】2021中提供用于創(chuàng)建和使用數(shù)據(jù)挖掘模型的集成環(huán)境。本教程使用的四種情況：有針對(duì)性的郵件預(yù)測(cè)；順序分析和聚類；演示。數(shù)據(jù)挖掘算法，并在SQLServer2021工具可以很容易地建立一個(gè)項(xiàng)目，包。括市場購物籃分析各種全面的解決方案，預(yù)測(cè)分析，有針對(duì)性的郵件分析。解決方案的情景更詳細(xì)的解釋在后面的教程。SQLServer2021最明顯的部分是用來創(chuàng)建和處理數(shù)據(jù)挖掘模型的工作室。器斷開連接的情況下建立一個(gè)服務(wù)項(xiàng)目分析。SQLServer管理工作室的主要職。欲了解更多關(guān)于從兩個(gè)。業(yè)智能開發(fā)工作室中選擇”。需觀眾的信息，請(qǐng)參看SQLServer聯(lián)機(jī)叢書中的“查看數(shù)據(jù)挖掘模型”。能夠確定哪些模式是最準(zhǔn)確的。為了建立數(shù)據(jù)預(yù)期，你將使用一種DME語言，DMX擴(kuò)展了傳統(tǒng)的SQL語法，AW公司生產(chǎn)并向北。頓Bothell完成，那里擁有500員工，以及一些地區(qū)銷售部門遍及各地。的產(chǎn)品根據(jù)子類別，型號(hào)和產(chǎn)品來分類。從這點(diǎn)來看，重點(diǎn)從SQLServer管理工作室的開發(fā)轉(zhuǎn)移到了維護(hù)和應(yīng)用。利用DTS設(shè)計(jì)器，您可以將包發(fā)布到服務(wù)器上并定期的運(yùn)
【正文】 y 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 supports only discrete or discretized attributes, and it considers all input attributes to be independent. The Microsoft Na239。ve Bayes algorithm produces a simple mining model that can be considered a starting point 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 states of the predicted attribute. Microsoft Time Series The 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 based 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 several months or years. A case may contain a set of variables (for example, sales at different stores). The Microsoft Time Series algorithm can use crossvariable correlations in its predictions. For example, prior sales at one store may be useful in predicting current sales at another store. Microsoft Neural Network In Microsoft SQL Server 2021 Analysis Services, the Microsoft Neural Network algorithm creates classification and regression mining models by constructing a multilayer perceptron work of neurons. Similar to the Microsoft 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 11 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。s performance for the next iteration, and so on. You can later use these probabilities to predict an oute of the predicted attribute, based on the input attributes. One of the primary differences between this algorithm and the Microsoft Decision Trees algorithm, however, is that its learning process is to optimize work 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 Regression The Microsoft Linear Regression algorithm is a particular configuration of the Microsoft Decision Trees algorithm, obtained by disabling splits (the whole regression formula is built in a single root node). The algorithm supports the prediction of continuous attributes. Microsoft Logistic Regression The Microsoft Logistic Regression algorithm is a particular configuration of the Microsoft Neural Network algorithm, obtained by eliminating the hidden layer. The algorithm supports the prediction of both discrete andcontinuous attributes.)
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