It's not a linear process that starts with one phase and works neatly through each step in strict order.
The key steps in Data mining process are as follows:
1) Data Cleaning- The first stage in data mining is data cleaning. It is crucial since using contaminated data in mining might confuse processes and yield false findings.
2) Data Integration- Data integration is the process of combining several heterogeneous data sources, such as databases, data cubes, or files, for analysis. This can aid in enhancing the data mining process' accuracy and speed.
3) Data Reduction - The size of the depiction is substantially reduced in volume while preserving integrity. Methods like Naive Bayes, Decision Trees, Neural Networks, etc. are used for data reduction.
4) Data Transformation- Data is changed in this process so that it can be used for data mining. Data is consolidated to make mining more effective and to make patterns more understandable.-
5) Data Mining- Data mining is a method for extracting knowledge and intriguing patterns from massive amounts of data. To extract the data patterns in these steps, intelligent patterns are used.
6) Pattern Evaluation- Using measurements of interestingness, this stage entails finding fascinating patterns that describe the knowledge. In order to make the data understandable to the user, data summarizing and visualization techniques are used.
7) Knowledge Representation- Data visualization and knowledge representation technologies are utilized in the knowledge representation stage to represent the mined data. Reports, tables, and other visual representations of data are used.
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