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Clustering Methods in Python

1. K-Means Clustering

K-Means clustering aims to partition the data into k clusters, where each data point belongs to the cluster with the nearest mean. It is a centroid-based algorithm that iteratively updates cluster centers to minimize the variance within each cluster.

Key Points:

  • Requires the number of clusters k to be specified.
  • Sensitive to initial placement of centroids.
  • Can converge to local minima.

2. Hierarchical Clustering

Hierarchical Clustering builds a hierarchy of clusters either agglomeratively (bottom-up) or divisively (top-down). Agglomerative clustering starts with each data point as its own cluster and merges the closest pairs until only one cluster remains.

Key Points:

  • Does not require the number of clusters to be specified initially.
  • Can produce a dendrogram, which is a tree-like diagram of clusters.
  • Computationally intensive for large datasets.

3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

DBSCAN is a density-based clustering algorithm that can find arbitrarily shaped clusters and identify outliers. It groups together points that are closely packed together and marks points that are far away as outliers.

Key Points:

  • Does not require the number of clusters to be specified.
  • Requires two parameters: eps (maximum distance between points in a cluster) and min_samples (minimum number of points in a cluster).
  • Can handle noise and outliers effectively.

4. Mean Shift Clustering

Mean Shift is a centroid-based algorithm that updates candidates for centroids to be the mean of the points within a given region. It does not require specifying the number of clusters in advance and can find the number of clusters automatically.

Key Points:

  • Automatically determines the number of clusters.
  • Computationally intensive for large datasets.
  • Sensitive to the bandwidth parameter.

5. Gaussian Mixture Model (GMM)

Gaussian Mixture Model (GMM) is a probabilistic model that assumes all the data points are generated from a mixture of several Gaussian distributions with unknown parameters. It uses the Expectation-Maximization (EM) algorithm to estimate the parameters.

Key Points:

  • Can handle clusters of different shapes and sizes.
  • Provides a probabilistic clustering.
  • Requires the number of clusters k to be specified.

Example Jupyter notebook