What Is Data Mining? (en)
Data Mining is the process of extracting useful information or patterns from large and complex datasets. Data mining uses techniques and algorithms to analyze data and identify hidden patterns, relationships, correlations, anomalies, or trends within the data. The main goal of data mining is to generate valuable and actionable insights from large and complex data.
Several commonly used data mining techniques include clustering, classification, association, regression, and anomaly detection.
- Clustering is a technique used to group data based on similarities in features or characteristics, so that similar data is clustered together.
- Classification is a technique used to classify data into predefined categories or classes.
- Association is a technique used to find relationships between features or items in a dataset, which can assist in analysis and recommendations.
- Regression is a technique used to predict the value of a continuous variable based on other variables.
- Anomaly detection is a technique used to identify data that deviates from existing patterns and can be useful for fraud detection or security analysis.
Data mining is widely used across various fields, including business, healthcare, education, science, and technology. Examples of data mining applications include sales prediction, credit analysis, health risk assessment, text mining, and social media analysis.