# Incremental Gaussian Mixture Clustering for Data Streams

arXiv: https://arxiv.org/abs/2412.07217 · HTML: https://mlsanity.com/paper/2412.07217/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 3 of 3)

- [Data Stream Clustering: A Review](https://mlsanity.com/paper/2007.10781/) (2020)
- [An Algorithm for Online K-Means Clustering](https://mlsanity.com/paper/1412.5721/) (2014)
- [Learning Mixture of Gaussians with Streaming Data](https://mlsanity.com/paper/1707.02391/) (2017)

## Abstract (arXiv)

The problem of analyzing data streams of very large volumes is important and is very desirable for many application domains. In this paper we present and demonstrate effective working of an algorithm to find clusters and anomalous data points in a streaming datasets. Entropy minimization is used as a criterion for defining and updating clusters formed from a streaming dataset. As the clusters are formed we also identify anomalous datapoints that show up far away from all known clusters. With a number of 2-D datasets we demonstrate the effectiveness of discovering the clusters and also identifying anomalous data points.