Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data. This tutorial will cover the basics of unsupervised learning, including common algorithms and techniques.

Common Unsupervised Learning Algorithms

  • Clustering Algorithms

    • K-Means
    • Hierarchical Clustering
    • DBSCAN

  • Dimensionality Reduction Techniques

    • Principal Component Analysis (PCA)
    • t-SNE
    • UMAP

K-Means Clustering

K-Means is one of the most popular clustering algorithms. It aims to partition the dataset into K distinct, non-overlapping subgroups (clusters) where each data point belongs to the cluster with the nearest mean.

How K-Means Works

  1. Initialize K centroids randomly.
  2. Assign each data point to the nearest centroid.
  3. Recompute the centroids as the mean of the points assigned to each cluster.
  4. Repeat steps 2 and 3 until the centroids do not change significantly.

K-Means Clustering

For more information on K-Means, you can read our detailed guide on K-Means Clustering.

Hierarchical Clustering

Hierarchical clustering is another popular clustering algorithm that builds a hierarchy of clusters. It can be either agglomerative (bottom-up) or divisive (top-down).

How Hierarchical Clustering Works

  1. Start with each data point as a separate cluster.
  2. Merge the closest clusters until there is only one cluster left.
  3. Alternatively, split the largest cluster until each cluster has only one data point.

Hierarchical Clustering

For more information on Hierarchical Clustering, you can read our detailed guide on Hierarchical Clustering.

Dimensionality Reduction

Dimensionality reduction is a technique used to reduce the number of variables in a dataset while retaining most of the original information.

PCA

Principal Component Analysis (PCA) is a widely used dimensionality reduction technique. It transforms the data into a new set of variables (principal components) that are uncorrelated and capture the maximum variance in the data.

PCA

For more information on PCA, you can read our detailed guide on PCA.

Conclusion

Unsupervised learning is a powerful tool for exploring and understanding data. By using techniques like clustering and dimensionality reduction, you can uncover hidden patterns and insights in your data.

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