| Data mining is described as a process of finding hidden precious data by evaluating the huge quantity of information stored in data warehouses, using various data mining techniques. The figure shows the data mining implementation steps: |
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- Business understanding: It focuses on understanding the project goals and requirements form a business point of view, then converting this information into a data mining problem afterward a preliminary plan designed to accomplish the target.
- Data understanding: Data understanding starts with an original data collection and proceeds with operations to get familiar with the data, to data quality issues, to find better insight in data, or to detect interesting subsets for concealed information hypothesis.
- Data preparation: It covers all operations to build the final data set from the original raw information.
- Modeling: In modeling, various modeling methods are selected and applied, and their parameters are measured to optimum values.
- Evaluation: It evaluates the model efficiently, and review the steps executed to build the model and to ensure that the business objectives are properly achieved.
- Deployment: Deployment refers to how the outcomes need to be utilized.
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Tom stole cameras when he worked here. I’ll hire him back when hell freezes over (never). |