Recap from Day 019

In day 019, we continued with common hard clustering algorithms. We saw that Hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis which seeks to build a hierarchy of clusters. And Self-Organizing Map is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map, and is therefore a method to do dimensionality reduction.

Today, we’ll start looking at Common Soft Clustering Algorithms

Common Soft Clustering Algorithms

Fuzzy c-Means

Fuzzy clustering is a form of clustering in which each data point can belong to more than one cluster.

How Fuzzy c-Means Works

“Partition-based clustering when data points may belong to more than one cluster.”

Source

Best Used…

• When the number of clusters is known

• For pattern recognition

• When clusters overlap

The fuzzy c-means algorithm is very similar to the k-means algorithm

Gaussian Mixture Model

A Gaussian mixture model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters.

How Gaussian Mixture Model Works

“Partition-based clustering where data points come from different multivariate normal distributions with certain probabilities”

Source

Best Used…

• When a data point might belong to more than one cluster

• When clusters have different sizes and correlation structures within them

Great Job. You made it to the end of day 020. I hope you found this informative. Thank you for taking time out of your schedule and allowing me to be your guide on this journey.

Reference

MathWorks- 90221_80827v00_machine_learning_section4_ebook_v03 pdf

*https://en.wikipedia.org/wiki/Fuzzy_clustering*

*http://scikit-learn.org/stable/modules/mixture.html*