Resources for learning about and implementing clustering algorithms. I will update the list on an ongoing basis.

• Clustering: A conceptual approach: In depth series of essays on clustering techniques. They assume no prior knowledge and are a fantastic place to start learning about clustering
• Document Clustering with Python: Contains implementations of the k-means and ward clustering algorithms. An example of multi-dimensional scaling, text processing and creating a tf-idf document matrix an added bonus!
• SciPy Hierarchical Clustering and Dendrogram Tutorial: Hands on tutorial on hierarchical clustering in Python
• SciKit learn clustering module: Implements a variety of clustering algorithms in Python including k-means, spectral clustering, ward hierarchical clustering, agglomerative clustering and DBSCAN. The homepage contains a helpful overview and comparison between clustering algorithms.
• SciPy dendrogram documentation: Useful visualisation tool for hierarchical clustering

I’m always looking to learn more. Please send suggestions or comments to contact [at] learningmachinelearning [dot] org