Introduction to Clustering Algorithms: DBSCAN, OPTICS, and HDBSCAN
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DBSCAN Pros and Cons Summary
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2023
Introduction to Clustering Algorithms: Gaussian Mixture Models
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Gaussian Mixed Model (GMM) refers to a linear combination of multiple Gaussian distribution functions. Gaussian Mixture Models are probabilistic models that assume all samples are derived from a mixture of an indefinite number of Gaussian distributions with unknown parameters. It belongs to the category of soft clustering methods in which each data point will be a member of each cluster in the dataset, but to varying degrees. This membership is assigned as a probability ranging from 0 to 1 of belonging to a certain cluster. Theoretically, GMM can fit any type of distribution, and is usually used to solve cases where data under the same set contains several different distributions (either the same type of distribution but with different parameters, or different types of distributions, such as normal and Bernoulli distributions).