Data Mining Implementation Process

Data mining is described as a process of finding hidden precious data by evaluating the huge quantity of information stored in data warehouses, using multiple data mining techniques such as Artificial Intelligence (AI), Machine learning and statistics.

Data mining implementation process consists of 6 steps:

1. 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.

Tasks:

• Determine business objectives
• Access situation
• Determine data mining goals
• Produce a project plan

2. 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.

Tasks:

• Collects initial data
• Describe data
• Explore data
• Verify data quality

3.Data Preparation

It takes more time. Covers all operations to build the final data set from the original raw information. Several times, data preparation is probable to be done

Tasks:

• Select data
• Clean data
• Construct data
• Integrate data
• Format data

4.Modeling:

In modeling, various modeling methods are selected and applied, and their parameters are measured to optimum values. Some methods gave particular requirements on the form of data. Therefore, stepping back to the data preparation phase is necessary.

Tasks:

• Select modeling technique
• Generate test design
• Build model
• Access model

5.Evaluation:

At the last of this phase, a decision on the use of the data mining results should be reached. It evaluates the model efficiently, and review the steps executed to build the model and to ensure that the business objectives are properly achieved. The main objective of the evaluation is to determine some significant business issue that has not been regarded adequately. At the last of this phase, a decision on the use of the data mining outcomes should be reached.

Tasks:

• Evaluate results
• Review process
• Determine next steps

6.Deployment

The concept of deployment in data mining refers to the appliance of a model for prediction using a new data. The deployment phase are often as simple as generating a report or as complex as implementing a repeatable data mining process.

Tasks

• Plan deployment
• Plan monitoring and maintenance
• Produce final report
• Review project