Andmekaevandamine

andmekaevandamine is the process of discovering patterns, correlations, and actionable insights in large data sets using methods from statistics, machine learning, and database systems. It aims to extract knowledge that is not readily apparent and that can inform decisions.

Techniques include supervised methods such as classification and regression, unsupervised methods such as clustering and dimensionality reduction, and pattern discovery such as association rules and anomaly detection. Common algorithms include decision trees, random forests, support vector machines, neural networks, k-means, and the Apriori algorithm. The selection depends on data type, task, and the required balance between accuracy and interpretability.

Data sources are diverse, ranging from structured relational databases and data warehouses to semi-structured logs and JSON, as well as unstructured text, images, and time-series data from sensors or financial markets. A critical step is data preparation: cleaning, integration, normalization, and feature engineering.

Applications span marketing analytics, customer segmentation, fraud detection, credit scoring, healthcare, bioinformatics, finance, supply chain optimization, and public policy. Data mining is usually part of a broader data science workflow involving data collection, model evaluation, deployment, and monitoring.

Ethical and legal considerations include privacy, consent, data governance, and compliance with regulations such as the GDPR. Techniques such as anonymization, differential privacy, and access controls are used to mitigate risk. Model bias, interpretability, and the potential for misuse are ongoing concerns.

Challenges include data quality, scalability to large datasets, imbalanced classes, concept drift, and the need for domain expertise to translate results into actionable decisions.