Data Mining is the process of discovering patterns, correlations, and anomalies within large datasets using statistical and computational techniques. It aims to extract useful information and insights that can be used for decision-making and predictive analysis. Key steps in data mining include:
- Data Cleaning: Removing noise and inconsistencies from the data.
- Data Integration: Combining data from multiple sources.
- Data Selection: Selecting relevant data for analysis.
- Data Transformation: Converting data into appropriate formats for mining.
- Pattern Evaluation: Identifying significant patterns and relationships.
Data Mining techniques include clustering, classification, regression, association rule learning, and anomaly detection. These techniques are applied in various fields, such as marketing, finance, healthcare, and cybersecurity, to uncover valuable insights and support strategic decisions.
