About mixed distribution clusters

This is an article about mixed distribution clusters.

Hello!

Today, we’re going to learn about mixed distribution clusters.

Mixed model clustering is an algorithm that clusters data from these mixed distributions assuming that data is generated from multiple probability distributions.

We will discuss mixed distribution clusters below.

principle of operation

Mixed model assumptions

Assuming that the data are generated from multiple probability distributions (such as Gaussian distributions), we estimate the weights and parameters of each distribution.

parameter estimation

We estimate the weights and parameters (mean, variance) of each probability distribution through Maximum Likelihood Estimation, etc.

Cluster allocation

Based on the estimated mixed model, each data is assigned to the most likely cluster.

Key Features

stochastic clustering

Mixed distribution clusters are based on probabilistic models, giving the probability that each data belongs to each cluster.

Model Complexity

Mixed distribution clusters can find different forms of clusters by tuning the complexity of the model.

Utilization

Outlier detection

The mixed distribution cluster takes into account which distribution the data is generated from, so it can be used effectively for outlier detection.

Natural language processing

It can be used to extract topics from a document, such as modeling topics.

Precautions

Select a Model

It is important to select the appropriate number of clusters and the parameters for each distribution.

Sensitive to outliers

Outliers can affect model estimation, so a response is needed.

at the end of the day

Mixed distribution clusters are a powerful algorithm for clustering data from these mixed distributions, assuming that data are generated from multiple probability distributions, and are actively used in various fields.

Thank you!