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Mapping Mining Structure Attributes to Source Columns
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When you define how you are going to use source columns in your mining structure and models, you are actually defining the content type of a column and a prediction clause. A prediction clause is quite simple; you just have to define which columns you are going to predict. You can use a column in a mining model in the following ways:
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The input of a model. The target variable of directed models. The target variable also serves as input.
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ignored column The column is treated as if it does not exist in the mining structure; it is ignored in a specific model.
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Column content types define how the column measures data values and whether the column is used as a key. You have already learned about different ways you can measure values. The supported content types are listed here:
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Discrete
The column has discrete values. The column has continuous values. You used this option to discretize continuous columns in your mining
continuous Discretized
structure.
Ordered
This is a discrete column that has ordered ranks.
cyclical This special kind of column contains values in a cyclical ordered set, such as numbers of days in a week.
Key columns correspond to and are mapped to a single column that represents a Primary Key in the source. However, you can have two additional key column types:
Key sequence column
A specific type of key having ordered values that represent a
sequence of events
Key time column A specific Key Sequence type in which values are ordered based on a time scale and are an equal distance apart
Working with SSAS Data Mining
using Case Table Definitions and nested Tables
If a column is a nested table, you have multiple options for how to use columns from the case table and columns from the nested table. In this section, you will look at several examples of how to use these columns. In the AdventureWorksDW2008 database, you have the dbo.vAssocSeqOrders and dbo.vAssocSeqLineItems views; you will use the first view as a case table and the second view as a nested table. Nested tables provide a lot of flexibility for modeling business problems. The following examples show you how to use the columns from the case table and the nested table from the views in different ways to answer different business questions.
Example 1: Association Rules
Table 9-2 shows the column usage for predicting associations. Note that the dbo.vAssocSeqLineItems view is used as a column of the case table that is, as a column of the dbo.vAssocSeqOrders view.
tabLe 9-2 Example 1: Case and Nested Table for Association Rules
case tabLe cOLumn
nesteD tabLe cOLumn
usage
Order Number Customer Key Region Income Group vAssocSeqLineItems Order Number Line Number Model
Key, input, predictable
In this example, you use the Model column of the nested table as a key, input, and predictable column. You are looking for the values of the Model column that predict other values of this column. This means you are searching for models that appear together on the same customer order; if they appear together many times, the appearance of one model predicts the appearance of another model. You are searching for the existence of rows of the nested table for each customer order; you do that through a row s key. Because a model is unique in an order, you can use it as a key. Because you want to predict the models, you use the Model column as both input and predictable.
Example 2: Sequences
You can use the Line Number attribute of the dbo.vAssocSeqLineItems view to define the sequence of purchases of models inside a single customer order. With multiple orders, you can try to find rules in sequences. Table 9-3 shows the usage of columns for predicting sequences.
Lesson 2: Creating Models and Applying Algorithms
tabLe 9-3 Example 2: Case and Nested Table for Sequence Clustering
case tabLe cOLumn
nesteD tabLe cOLumn
usage
Order Number Customer Key Region Income Group vAssocSeqLineItems Order Number Line Number Model
Key Sequence, input Input, predictable
This example uses Line Number as the Key Sequence column, indicating that it has a sequence in it. Because the sequence predicts a model, you use this column as input. The Model column is predictable, but because you want to find not just sequences, but sequences of models, you have to use it as input as well.
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