What is knowledge discovery process in data mining and knowledge management? And what is the difference and similarity between knowledge discovery and data mining?
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Amen Answered question 23/02/2023
Knowledge discovery process is a systematic process that involves extracting useful insights, patterns, and knowledge from large volumes of data through data mining and knowledge management techniques. The process typically involves several stages, including:
- Data preparation: This involves collecting, cleaning, and transforming the data into a format that is suitable for analysis. This may involve removing duplicates, filling in missing values, and converting data types.
- Data exploration: This involves analyzing the data to gain insights into its characteristics, such as its distribution, correlation, and outliers. This helps in identifying potential patterns and relationships in the data.
- Data modeling: This involves selecting appropriate algorithms and models for analyzing the data and building predictive models. This may involve applying techniques such as decision trees, neural networks, or clustering algorithms.
- Evaluation: This involves testing and validating the models to ensure their accuracy and usefulness in predicting outcomes. This may involve using metrics such as precision, recall, or accuracy.
- Knowledge dissemination: This involves communicating the results of the analysis to stakeholders in a format that is easily understandable and actionable. This may involve generating reports, dashboards, or visualizations.
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Amen Answered question 23/02/2023