What is the Difference Between KDD and Data mining?
🆚 Go to Comparative Table 🆚The main difference between Knowledge Discovery in Databases (KDD) and Data Mining lies in their scope and the specific steps involved in each process. Here are the key differences:
- Scope: KDD is the overall process of extracting knowledge from data, while Data Mining is a step inside the KDD process that focuses on identifying patterns in data.
- Iterative Process: KDD is an iterative process where evaluation measures can be enhanced, mining can be refined, and new data can be integrated and transformed to get different and more appropriate results.
In summary, KDD is a broader process that includes Data Mining as one of its steps. Data Mining is the application of specific algorithms to extract patterns from data, while KDD focuses on discovering new knowledge from data as a whole. Both processes are essential for extracting valuable insights from large datasets and making better decisions based on the patterns found in the data.
Comparative Table: KDD vs Data mining
Here is a table comparing Knowledge Discovery in Databases (KDD) and Data Mining:
| Feature | KDD (Knowledge Discovery in Databases) | Data Mining |
|---|---|---|
| Objective | To find useful knowledge from data | To extract useful information from data |
| Scope | Comprehensive approach to extracting useful knowledge and insights from large datasets | A specific task within the KDD process that involves identifying hidden trends, and relationships in data |
| Techniques Used | Includes data preprocessing, data transformation, pattern evaluation, and knowledge representation | Involves data preprocessing, modeling, and analysis |
| Focus | Emphasizes developing appropriate methods or tools to extract previously unknown knowledge from large collections of digitized data | Emphasizes identifying patterns, trends, variations, and correlations in big data |
In summary, KDD is a broader process that encompasses data mining as one of its components. Data mining is a specific task within the KDD process that focuses on finding patterns and relationships in data.
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