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Overviewing and preparing the data is probably the most exhaustive part of a data mining project To get a comprehensive overview of your data, you can use many different techniques and tools You can start with SQL queries and reports, or you can use UDM cubes In addition, you can use descriptive statistics such as frequency distribution for discrete variables, and the mean value and the spread of the distribution for continuous variables You can use Data Source View for a quick overview of your variables in table, pivot table, graph, or pivot graph format Microsoft Office Excel statistical functions, pivot tables, and pivot graphs are useful tools for data overview as well After you understand your data, you have to prepare it for data mining You have to decide what exactly your case is Sometimes this is a simple task; sometimes it can get quite complex.

For example, a bank might decide that a case for analysis is a family, whereas the transaction system tracks data about individual persons only After you define your case, you prepare a table or a view that encapsulates everything you know about your case You can also prepare child tables or views and use them as nested tables in a mining model For example, you can use an orders header production table as the case table, and an order details table as a nested table if you want to analyze which products are purchased together in a single order Usually, you also prepare some derived variables In medicine, for example, the obesity index is much more important for analyses than a person s bare height and weight You have to decide what to do with missing values, if there are too many.

When you ve finished creating the message, all you need to do to send it is use the send command For example, the following command sends the message referenced by the variable my_mess:

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For example, you can decide to replace them with mean values You should also check the outliers rare and far out-of-bounds values in a column You can group or discretize a continuous variable in a limited number of bins and thus hide outliers in the first and the last bin..

Mail uses the message object to represent an incoming message (as opposed to the outgoing message object that represents an outgoing message) Table 13-8 explains the properties of the message object message Property

SSAS 2008 supports all of the most popular data mining algorithms. In addition, SSIS includes two text mining transformations. Table 1 summarizes the SSAS algorithms