medium
2 min read
GMM models
GMM Models GMM models are a powerful and widely used technique in data mining for clustering. These models allow us to group data points into clusters based...
Quick Actions
Insights
Difficultymedium
Reading Time2 min
GMM Models GMM models are a powerful and widely used technique in data mining for clustering. These models allow us to group data points into clusters based...
GMM Models
GMM models are a powerful and widely used technique in data mining for clustering. These models allow us to group data points into clusters based on their similarities. Each cluster represents a distinct group of data points with similar characteristics.
Key Concepts:
Centroids: Centers of the clusters, representing the center point of each group.
Covariance matrix: A measure of how similar the data points in a cluster are to each other.
Cluster inertia: The measure of how tightly the data points in a cluster are grouped together.
Parameters: The model parameters include the number of clusters (k), the location of the centroids, and the covariance matrix.
How GMM Models Work:
Initialization: Choose the number of clusters (k) and initialize the centroids randomly in the data space.
Iteration:
For each data point, calculate its distance to each centroid.
Assign the data point to the cluster with the closest centroid.
Update the centroids by calculating their average positions.
When the centroids are sufficiently close to each other (e.g., within a specified distance), stop the iterations.
Return the cluster labels for each data point.
Advantages of GMM Models:
Can handle a large number of data points.
Robust to outliers.
Provides a visual representation of clusters.
Disadvantages of GMM Models:
Can be sensitive to the choice of initial centroids.
May not be suitable for high-dimensional data.
Can be computationally expensive for large datasets