Un-supervised

Clustering - hierarchical clustering, k-means, mixture models, DBSCAN, and OPTICS algorithm
Anomaly Detection - Local Outlier Factor, and Isolation Forest
Dimensionality Reduction - Principal component analysis, Independent component analysis, Non-negative matrix factorization, Singular value decomposition

Algorithms

Name	Comments on Applicability	Reference
Hierarchical Clustering	(N-1) combination of clusters are formed to choose from. Expensive and slow. n×n distance matrix needs to be made. Cannot work on very large datasets. Results are reproducible. Does not work well with hyper-spherical clusters. Can provide insights into the way the data pts. are clustered. Can use various linkage methods(apart from centroid).
k-means	Pre-specified number of clusters. Less computationally intensive. Suited for large dataset. Point of start can be random which leads to a different result each time the algorithm runs. K-means needs circular data. Hyper-spherical clusters. K-Means simply divides data into mutually exclusive subsets without giving much insight into the process of division. K-Means uses median or mean to compute centroid for representing cluster.
Gaussian Mixture Models

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