Karteikarten: Data Mining Techniques and Applications — 16 Karten

Classification analysis — definition?

Finds models to categorize data.

Clustering analysis — role?

Groups similar data points without labels.

Association rule learning — purpose?

Discovers item correlations in data.

Outlier detection — function?

Identifies anomalous data points.

Sequential pattern mining — focus?

Finds ordered sequences and trends.

Data mining — applications?

Supports decision-making across industries.

Healthcare data mining — use?

Detects disease-treatment relationships and fraud.

Data mining process — steps?

Includes understanding, preparation, modeling, evaluation, deployment.

Classification models — techniques?

Decision trees, rules, neural networks.

Clustering — goal?

Maximize intra-group similarity.

Association rules — example?

Items bought together frequently.

Outlier detection — domains?

Fraud, intrusion, fault detection.

Sequential pattern mining — benefit?

Forecasts future events from past sequences.

Data mining — industry sectors?

Healthcare, finance, marketing, security.

Data mining in marketing — purpose?

Identify purchasing patterns and optimize campaigns.

Data mining — importance?

Uncovers insights to inform decisions.