What are some of the pros and cons of GMMs?
Pros: Has the ability to find more local clusters that K-Means would not be able to differentiate
Unsupervised Learning
How does the EM algorithm (in the context of GMM) compare to K-Means?
K-Means aims to minimize the Within Cluster Sum of Squares, while EM aims to maximize the likelihood of an underlying probability distribution.
What are some options for identifying the number of components in a GMM?
When used for clustering, any of the evaluation metrics (Silhouette Score, Dunn Index, Rand Index, etc.) are appropriate
What is a Gaussian Mixture Model (GMM)?
A Gaussian Mixture Model describes an underlying distribution that is composed of multiple individual Gaussian distributions
What is Expectation-Maximization (EM)?
Expectation-Maximization refers to a two-step, iterative process that is often used when latent or unobserved variables are present underlying a data generation process.
What is Spectral co-clustering?
Spectral Co-Clustering is an implementation of Co-Clustering that models the input data as a bipartite graph
What is Bi-Clustering? What are possible use cases of it?
Bi-Clustering, or Co-Clustering seeks to simultaneously perform clustering both within the observations and columns of a dataset.
What is Spectral Clustering?
Spectral clustering is an alternative clustering technique that is rooted in graph theory.
How does DBSCAN Clustering work, and in what cases is it useful?
Density-based clustering approaches, such as DBSCAN, tend to perform better than partitioning methods like K-Means when clusters are non-globular
How is clustering affected by high-dimensional data, and how can the quality of clusters generated be improved in such cases?
One problem of performing clustering in high-dimensional data is that common distance metrics, such as Euclidean distance, do not perform as well.