Welcome to the tutorial on Unsupervised Learning! Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data. This means that the data does not have any predefined categories or labels.
Key Concepts
- Clustering: Grouping data points into clusters based on their similarity.
- Dimensionality Reduction: Reducing the number of variables in a dataset while retaining the important information.
- Association Rules: Discovering interesting relationships between variables in large databases.
Common Algorithms
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- t-SNE
- Apriori Algorithm
K-Means Clustering
K-Means clustering is one of the most popular clustering algorithms. It aims to partition the dataset into K pre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group.
For more information on K-Means Clustering, check out our K-Means Clustering Tutorial.
PCA
PCA is a dimensionality reduction technique that is often used to reduce the dimensionality of large datasets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.
For more information on PCA, visit our PCA Tutorial.