1. Apriori Algorithm: The Apriori algorithm is used for association rule mining. It identifies frequent itemsets in a dataset and generates association rules based on these itemsets.

2. K-means Clustering Algorithm: The K-means clustering algorithm is used for clustering analysis. It partitions a dataset into K clusters based on similarity between data points.

3. Decision Tree Algorithm: The decision tree algorithm is used for classification and regression analysis. It builds a tree-like model of decisions and their possible consequences.

4. Support Vector Machine Algorithm: The support vector machine algorithm is used for classification and regression analysis. It uses support vectors to classify data points or make predictions.

5. Naive Bayes Algorithm: The Naive Bayes algorithm is used for classification analysis. It calculates the probability of a data point belonging to each class based on its features and assigns the data point to the class with the highest probability.

6. Random Forest Algorithm: The random forest algorithm is used for classification and regression analysis. It combines multiple decision trees to make predictions.

7. Collaborative Filtering Algorithm: The collaborative filtering algorithm is used for recommendation systems. It analyzes user preferences and makes recommendations based on similarities between users or items.

8. K-nearest Neighbors Algorithm: The k-nearest neighbors algorithm is used for classification and regression analysis. It assigns a data point to the class of its k nearest neighbors.

9. Genetic Algorithm: The genetic algorithm is used for optimization problems. It iteratively evolves a population of candidate solutions to find the best solution.

10. Neural Network Algorithm: The neural network algorithm is used for classification and regression analysis. It simulates the behavior of a biological brain to make predictions based on input data.