Spectral Clustering for beginners – Towards Data Science

Clustering is one of the most widely used techniques for exploratory data analysis. Its goal is to divide the data points into several groups such that points in the same group are similar and points in different groups are dissimilar to each other. Spectral clustering has become increasingly popular due to its simple implementation and promising performance in many graph-based clustering. It can be solved efficiently by standard linear algebra software, and very often outperforms traditional algorithms such as the k-means algorithm. Here, we will try to explain very briefly how it works!