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【正文】 d 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 Association The Microsoft Association algorithm is specifically designed for use in market basket analyses. The algorithm considers each attribute/value pair (such as product/bicycle) as an item. An itemset is a bination of items in a single transaction. The algorithm scans through the dataset trying to find itemsets that tend to appear in many transactions. The SUPPORT parameter defines how many transactions the itemset must appear in before it is considered significant. For example, a frequent itemset may contain {Gender=Male, Marital Status = Married, Age=3035}. Each itemset has a size, which is number of items it contains. In this case, the size is 3. Often association models work against datasets containing nested tables, such as a customer list followed by a nested purchases table. If a nested table exists in the dataset, each nested key (such as a product in the purchases table) is considered an item. 16 16 The Microsoft Association algorithm also finds rules associated with itemsets. A rule in an association model looks like A, B=C (associated with a probability of occurring), where A, B, C are all frequent itemsets. The 39。=39。 implies that C is predicted by A and B. The probability threshold is a parameter that determines the minimum probability before a rule can be considered. The probability is also called confidence in data mining literature. Association models are also useful for cross sell or collaborative filtering. For example, you can use an association model to predict items a user may want to purchase based on other items in their basket. Microsoft Sequence Clustering The Microsoft Sequence Clustering algorithm analyzes sequenceoriented data that contains discretevalued series. Usually the sequence attribute in the series holds a set of events with a specific order (such as a click path). By analyzing the transition between states of the sequence, the algorithm can predict future states in related sequences. The Microsoft Sequence Clustering algorithm is a hybrid of sequence and clustering algorithms. The algorithm groups multiple cases with sequence attributes into segments based on similarities of these sequences. A typical usage scenario for this algorithm is Web customer analysis for a portal site. A portal Web site has a set of affiliated domains such as News, Weather, Money, Mail, and Sport. Each Web customer is associated with a sequence of Web clicks on these domains. The Microsoft Sequence Clustering algorithm can group these Web customers into moreorless homogenous groups based on their navigations patterns. These groups can then be visualized, providing a detailed understanding of how customers are using the site. 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 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. 17 17 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 and continuous attributes. Working Through the Tutorial Throughout this tutorial you will work in Business Intelligence Development Studio (as depicted in