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advance. You examine data with advanced mathematical methods, using data mining algorithms, and then you examine patterns and rules that your algorithms find. The SSAS data mining engine runs the algorithms automatically after you set up all of the parameters you need; therefore, you can check millions of different pivoting options in a limited time. In this chapter, you're going to learn how to perform data mining analyses with SSAS 2008.

To add multiple recipients of the same type, use a make new statement for each recipient You can also use a repeat loop

The first question you may ask yourself is what the term data mining means In short, data mining enables you to deduce hidden knowledge by examining, or training, your data with data mining algorithms Algorithms express knowledge found in patterns and rules Data mining algorithms are based mainly on statistics, although some are based on artificial intelligence and other branches of mathematics and information technology as well Nevertheless, the terminology comes mainly from statistics What you re examining is called a case, which can be interpreted as one appearance of an entity, or a row in a table The attributes of a case are called variables After you find patterns and rules, you can use them to perform predictions In SSAS 2008, the DM model is stored in the SSAS database as a kind of a table.

Many traders like Bollinger Bands because they are flexible enough to be used on many types of securities, not just stocks When you look at Bollinger Bands on a stock chart, you ll see an upper and lower band, most likely in blue or black There is also a dotted line in the middle between the bands This is actually the default 20-day simple moving average In addition, and this is important, you ll see the market price bouncing along between the upper and lower bands When the market price tags or pierces the upper band, it means that the market is overbought (ie, there has been too much buying) What many people don t realize is that the market can remain overbought for some time before changing direction Just because it touches the upper band doesn t mean it will suddenly reverse Put another way, the market can remain overbought longer than you can remain solvent, to paraphrase economist John Maynard Keynes

It s not a table in a relational sense, as it can include nested tables in columns In the model, the information about the variables, algorithms used, and the parameters of the algorithms are stored Of course, after the training, the extracted knowledge is stored in the model as well The data used for training isn t part of the model, but you can enable drillthrough on a model, and use drillthrough queries to browse the source data Most of the literature divides DM techniques into two main classes: directed algorithms and undirected algorithms With a directed approach, you have a target variable that supervises the training in order to explain its values with selected input variables Then the directed algorithms apply gleaned information to unknown examples to predict the value of the target variable.

When you need to send a file as an attachment, use a make new attachment command to tell the content object of the message object to add the attachment and where to put it The attachment class has only one property, file name, which you use to specify the path and name of the file you want to attach For example, the following statement assigns an alias to the file Macintosh HD: Users:dan:Documents:User Guidepdf to a variable named myFile, which it then uses to attach the file to the message referenced by the variable my_mess:

With the undirected approach, you re trying to discover new patterns inside the dataset as a whole, without any specific target variable For example, you use a directed approach to find reasons why users purchased an article and an undirected approach to find out which articles are commonly purchased together You can answer many business questions with data mining Some examples include the following: A bank might ask what the credit risk of a customer is A customer relationship management (CRM) application can ask whether there are any interesting groups of customers based on similarity of values of their attributes A retail store might be interested in which products appear in the same market basket A business might be interested in forecasting sales..