Creating a New Mining Structure
When you build a data mining solution in Microsoft SQL Server Analysis Services, the first step is to create a mining structure by using the Data Mining Wizard (Analysis Services - Data Mining) in Business Intelligence Development Studio. The mining structure defines the data domain from which mining models are built. All mining models are based on a structure.
Mining structures use either relational or online analytical processing (OLAP) data sources. Relational mining structures are based on data that is stored in relational database systems, defined as a data source view. OLAP mining structures are based on a dimension and related measures from an OLAP cube that exists on the same database as the mining structure.
For More Information:Designing Databases, Designing Analysis Services Multidimensional Database Objects
The Data Mining Wizard automatically defines a mining structure, and lets you add an initial mining model to the structure. Because a mining structure can contain multiple mining models, you can use Data Mining Designer to add more mining models to the structure.
The following sections provide more information about how to create new mining structures with the Data Mining Wizard, and how to set options on the mining structure that will let you create a test set or run queries on the data in the mining structure.
Relational mining structures can be based on any data that is available through an OLE DB data source. If the source data is contained within multiple tables, you can feed it into the wizard as a single case table by using nested tables.
For More Information:Nested Tables (Analysis Services - Data Mining)
The Data Mining Wizard guides you through the following steps to create the structure for a new mining model:
Selecting a data source type, in this case a relational database.
Deciding whether to build just a structure, or a structure with a mining model.
Selecting an algorithm for the model.
Selecting a data source.
Selecting a case table and, optionally, any nested tables.
Selecting the type for each column; predictable, input, or key.
Specifying the column content types.
Specifying an optional holdout data set.
Enabling drillthrough on the structure; naming and saving the new mining structure and the associated mining model.
OLAP cubes frequently contain so many members and dimensions that it can be difficult to know out where to begin with data mining. To help identify the patterns that the cubes contain, typically you identify a single dimension of interest, and then begin to explore patterns related to that dimension. The following table lists several common OLAP data mining tasks, describes sample scenarios in which you might apply each task, and identifies the data mining algorithm to use for each task.
Group members into clusters
Segment a customer dimension based on customer member properties, the products that the customers buy, and the amount of money that the customers spend.
Microsoft Clustering Algorithm
Find interesting or abnormal members
Identify interesting or abnormal stores in a store dimension based on sales, profit, store location, and store size.
Microsoft Decision Trees Algorithm
Find interesting or abnormal cells
Identify store sales that go against typical trends over time.
Microsoft Time Series Algorithm
The Data Mining Wizard guides you through the following process to create the structure for a new mining model:
Selecting a data source type, in this case a cube.
Selecting an algorithm.
Selecting a source cube dimension.
Selecting a case key.
Selecting case columns.
Selecting any nested tables.
Selecting the usage for each column; predictable, input, or key.
Specifying the column content types.
Slicing the source cube.
Creating an optional test data set.
Naming and saving the new mining structure and the associated mining model.
You can set the following options on the last page of the wizard:
Create mining model dimension
Create a cube using mining model dimension
If you choose to create a new mining model dimension in the source cube, you can include the information that the data mining algorithm finds in the OLAP data source. By creating a mining model dimension, you can browse and query the model content, in the form of a dimension. This option is available for models that are built by using the Microsoft Clustering, the Microsoft Decision Trees, and Microsoft Association Rules algorithms.
If you select the option to create a new cube, a new cube is defined on the database that includes the mining model dimension, and optionally any related dimensions.
When you create the mining structure, you must also set two important options for working with the data: holdout and drillthrough. Holdout is a SQL Server 2008 feature that lets you partition the data in the mining structure into a training set and a testing set, for use with all models associated with that structure. For more information, see Partitioning Data into Training and Testing Sets (Analysis Services - Data Mining).
Drillthrough lets you view source data in the mining structure by querying the mining model. This is useful when you are viewing the results of a mining model, and want to see additional details from the underlying cases. For example, you may want to find contact information, the cases that were used to a train a particular cluster, and so forth. To use drillthrough, you must enable it when you create the mining structure; you cannot enable it later. For more information, see Using Drillthrough on Mining Models and Mining Structures (Analysis Services - Data Mining).