The tutorials in this learning path were created using Oracle SQL Developer version 19.3 and Oracle Database Production 22.214.171.124.0. Oracle Machine Learning User Interface (Data Miner) 18.4 is included with Oracle SQL Developer. You may use an older (or newer version) of Oracle Database, Oracle SQL Developer and Oracle Data Miner, however, just note that your results and the screenshots may not match exactly.
The Oracle Machine Learning User Interface (Data Miner) graphical user interface (GUI) is included as a free extension of Oracle SQL Developer. In order to use the Oracle Data Miner GUI to perform data mining, you must complete the following three setup tasks:
Note: You do not need any experience with SQL Developer in order to perform the required steps.
This lesson focuses on a business problem that can be solved by applying a Classification model. In our scenario, ABC Company wants to identify customers who are most likely to purchase insurance.
Note: For the purposes of this tutorial, the "Data and Acquisition" phase has already been completed, and the sample data set contains all required data fields. Therefore, this lesson focuses primarily on the "Building and Evaluation of Models" phase.
This lesson focuses on a text mining problem that can be solved by applying a Clustering model using the EM algorithm. In our scenario, ABC Company wants to use the data from customer feedback to predict the kind of group (or cluster) to which a customer tends to belong.
To accomplish this goal, you build a workflow that:
In this lesson, you focus on a business problem that can be solved by applying a Classification model. In our scenario, ABC Company wants to know which customer attributes are most significant in predicting the gender of a customer. The new feature selection / generation enhancements are used as part of this mining exercise.
In this new workflow, you:
Data mining can be used to solve many kinds of predictive analysis problems, including the following:
Oracle Data Miner provides predictive query capabilities for these specific model types. The predictive query options enable dynamic scoring of these model by generating a transient model that is not persisted.
In this lesson, you create a workflow that imports JSON data by using the JSON Query node. The JSON Query node enables you to selectively query desirable attributes and project the result in relational format. Once the data is in relational format, you can treat it as a normal relational data source and start analyzing and mining it immediately.
In the workflow, you:
Use these links to learn more about Oracle Data Miner.
Oracle Data Mining Concepts (19c Online Documentation)
Oracle Data Miner Installation Administration (19c Online Documentation)
Oracle Data Miner User Guide (19c Online Documentation)
Oracle Advanced Analytics (Oracle Technology Network)
Oracle Data Mining (Oracle Technology Network)