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Aug 14, 14 tweets

The 10 types of clustering that all data scientists need to know.

Let's dive in:

1. K-Means Clustering:

This is a centroid-based algorithm, where the goal is to minimize the sum of distances between points and their respective cluster centroid.

2. Hierarchical Clustering:

This method creates a tree of clusters. It is subdivided into Agglomerative (bottom-up approach) and Divisive (top-down approach).

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

This algorithm defines clusters as areas of high density separated by areas of low density.

4. Mean Shift Clustering:

It is a centroid-based algorithm, which updates candidates for centroids to be the mean of points within a given region.

5. Gaussian Mixture Models (GMM):

This method uses a probabilistic model to represent the presence of subpopulations within an overall population without requiring to assign each data point to a cluster.

6. Spectral Clustering:

It uses the eigenvalues of a similarity matrix to reduce dimensionality before applying a clustering algorithm, typically K-means.

7. OPTICS (Ordering Points To Identify the Clustering Structure):

Similar to DBSCAN, but creates a reachability plot to determine clustering structure.

8. Affinity Propagation:

It sends messages between pairs of samples until a set of exemplars and corresponding clusters gradually emerges.

9. BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies):

Designed for large datasets, it incrementally and dynamically clusters incoming multi-dimensional metric data points.

10. CURE (Clustering Using Representatives):

It identifies clusters by shrinking each cluster to a certain number of representative points rather than the centroid.

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