Data mining — Easy Mining procedures for basic mining steps

Easy Mining procedures for basic mining steps

The Easy Mining procedures for basic mining steps correspond to the SQL API of Intelligent Miner®. They are easy to use because their syntax is easy. Furthermore, they concentrate on the more frequently used concepts of SQL/MM. They might even provide better results compared to the Easy Mining procedures for typical mining tasks because you can modify the parameters yourself.

However, modifying the parameters yourself means that you need knowledge about the data mining process. For example, you must know how to modify the maximum number of clusters to gain better clustering results.

With the Easy Mining procedures for basic mining steps, you can create, test, and modify data models. You can later apply these data models to new data to help you make successful business decisions.

The following table provides an overview of the available Easy Mining procedures for basic mining steps:

Table 1. Overview of the Easy Mining procedures for basic mining steps
Tasks	Classification mining procedure	Regression mining procedure	Clustering mining procedure	Associations mining procedure	Sequences mining procedure	Time series mining procedure
Building models	BuildClasModel	BuildRegModel	BuildClusModel	BuildRuleModel	BuildSeqRuleModel	BuildTsModel
Testing models	TestClasModel	TestRegModel	-	-	-	-
Applying models	ApplyClasModel	ApplyRegModel	ApplyClusModel	ApplyRuleModel	ApplySeqRuleModel	-
Exporting models	ExportClasModel	ExportRegModel	ExportClusModel	ExportRuleModel	ExportSeqRuleModel	ExportTsModel
Exporting test result	ExportClasTestResult	ExportRegTestResult	-	-	-	-
Building mining views	BuildClasView	BuildRegView	BuildClusView	BuildRuleView	BuildSeqRuleView	-

Easy Mining procedures for classification mining steps
Classification and regression are prediction techniques. This means that the Classification mining function and the Regression mining function learn from known cases to predict an outcome in similar situations. Often cases are known from the past experience and applied to new cases when it can be assumed that the situation has not greatly changed. The outcome of classification is a categorical value, the outcome of regression is a numeric value. Apart from this difference, classification and regression are similar.
Easy Mining procedures for regression mining steps
Regression and classification are prediction techniques. This means that the Regression mining function and the Classification mining function learn from the past to predict an outcome in the future. The outcome of regression is a numeric value, the outcome of classification is a categorical value. Apart from this difference, regression and classification are similar.
Easy Mining procedures for clustering mining steps
The Clustering mining function discovers homogenous groups of records in your data. These groups are called clusters or segments. These clusters can represent, for example, customers who share a common buying behavior or stores with common sales characteristics.
Easy Mining procedures for associations mining steps
The goal of the Associations mining function is to find items that are consistently associated with each other in a meaningful way. For example, you can analyze purchase transactions to discover combinations of goods that are often purchased together. The Associations mining function answers the question: If certain items are present in a transaction, what other item or items are likely to be present in the same transaction? The relationships discovered by the Associations mining function are expressed as association rules.
Easy Mining procedures for sequences mining steps
With sequence rules, you can predict what comes next. In the retail business area, you can make targeted product offerings based on the purchase histories of your customers. For example, if a customer has ordered a digital camera, you can send a leaflet about the latest memory card offerings together with the invoice to the customer.
Easy Mining procedures for time series mining steps
With the Easy Mining procedures for time series forecasts, you can build and export time series models. Typical examples are forecasting future sales of a product, given past sales. You can apply time series prediction to any numeric column in a DB2® table.
Exporting models or test results
You can apply models or test results to data that is stored in a different database by exporting the models or test results and transferring them to the other database using a variety of procedures. This section describes the procedures, the syntax and input parameters and an example.

Parent topic: Data Mining with the Easy Mining procedures