Unsupervised learning is a type of machine learning where the algorithm is given a dataset without any labels or explicit instructions on what to do with the data. The algorithm must learn by itself to identify patterns and relationships in the data.

Key Concepts

  • Clustering: Grouping similar data points together.
  • Dimensionality Reduction: Reducing the number of variables in a dataset.
  • Association Rules: Discovering interesting relationships between variables in large databases.

Types of Unsupervised Learning

  • Clustering: K-means, hierarchical clustering, DBSCAN.
  • Dimensionality Reduction: Principal Component Analysis (PCA), t-SNE, Autoencoders.
  • Association Rules: Apriori, Eclat.

Example: K-means Clustering

K-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. K-means clustering aims to partition the dataset into k pre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group.

K-means Algorithm Steps

  1. Initialization: Choose k initial centroids randomly from the data points.
  2. Assignment: Assign each data point to the nearest centroid.
  3. Update: Recompute the centroids as the mean of the assigned data points.
  4. Repeat: Steps 2 and 3 until convergence.

K-means Clustering

Further Reading

For more information on unsupervised learning, you can explore the following resources: