Alternatives in Spectral Clustering:

The talk is the exposition of my undergraduate paper for the degree of Mathematician. It exposes 3 of the main clustering algorithms used in the literature (K-means, agglomerative hierarchical clustering and DBSCAN), explains the construction and theoretical validity of the Spectral Clustering algorithm, where a variation in its construction is presented, and computationally evaluates the mentioned algorithms for various dimensions and data configurations, introducing similarity metrics between partitions to measure effectiveness in these algorithms.