Classification: Assigning items to predefined categories or classes based on their attributes. Common algorithms include decision trees, random forests, and support vector machines (SVM).
- Regression: Predicting a continuous value based on input variables. Linear regression and logistic regression are widely used techniques.
- Clustering: Grouping similar items together without predefined labels. Algorithms such as k-means, hierarchical clustering, and DBSCAN are commonly used.
- Association Rule Learning: Discovering interesting relationships or associations between variables. The Apriori algorithm is often used for finding frequent item sets and association rules.
- Anomaly Detection: Identifying china data unusual or outlier data points that do not conform to the expected pattern. Techniques include statistical methods, isolation forests, and neural networks.
- Sequential Pattern Mining: Finding patterns in data where the order of events matters. This is useful in applications like market basket analysis, where the sequence of purchases is important.
Applications of Data Mining
- Business Intelligence: Data mining helps companies understand customer behavior, identify market trends, and make strategic decisions. Techniques like market basket analysis can reveal product purchase patterns.
- Healthcare: In healthcare, data mining can predict disease outbreaks, personalize treatments, and improve patient outcomes by analyzing large sets of medical records and clinical data.
- Finance: Financial institutions use data mining for credit scoring, fraud detection, risk management, and investment forecasting.