Clustering Challenges

Practice implementing and optimizing clustering algorithms to discover patterns in unlabeled data.

Beginner

K-Means Clustering Implementation

Implement the K-means clustering algorithm from scratch and apply it to discover patterns in unlabeled data.

K-means
Unsupervised Learning
Python
Coming Soon
Intermediate

Hierarchical Clustering

Implement agglomerative hierarchical clustering with different linkage methods and visualize dendrograms.

Hierarchical Clustering
Dendrograms
Linkage Methods
Advanced

DBSCAN Implementation

Implement the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm.

DBSCAN
Density-Based Clustering
Noise Handling
Coming Soon
Advanced

Gaussian Mixture Models

Implement Gaussian Mixture Models using the Expectation-Maximization algorithm for soft clustering.

GMM
EM Algorithm
Soft Clustering