What data mining techniques are used for large datasets?

Data mining techniques used for large datasets include clustering, classification, regression, association rules, and anomaly detection.

Clustering is a technique used to group similar data points together. This is particularly useful in large datasets as it can help to identify patterns and trends that may not be immediately obvious. For example, a retailer might use clustering to identify groups of customers with similar buying habits. There are various algorithms used for clustering, such as K-means, hierarchical clustering, and DBSCAN.

Classification is another common technique used in data mining. This involves predicting the class or category of a data point based on its features. For instance, a bank might use classification to predict whether a customer is likely to default on a loan based on their credit history and income. Decision trees, random forests, and support vector machines are among the algorithms used for classification.

Regression is a technique used to predict a continuous outcome variable based on one or more input variables. For example, a real estate company might use regression to predict house prices based on features like location, size, and age of the property. Linear regression and logistic regression are commonly used algorithms for this purpose.

Association rules are used to discover relationships between variables in large datasets. This technique is often used in market basket analysis, where the goal is to find associations between products that are frequently bought together. The Apriori algorithm is a popular method for generating association rules.

Anomaly detection is a technique used to identify unusual data points that deviate significantly from the rest of the dataset. These anomalies could represent errors, fraud, or other significant events. There are various methods for anomaly detection, including statistical methods, machine learning algorithms, and distance-based methods.

Each of these techniques has its strengths and weaknesses, and the choice of technique often depends on the specific characteristics of the dataset and the goals of the analysis. It's also worth noting that these techniques are often used in combination, as part of a broader data mining strategy.