# K-means for Evolving Data Streams

arXiv: https://arxiv.org/abs/2012.03628 · HTML: https://mlsanity.com/paper/2012.03628/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [DeDrift: Robust Similarity Search under Content Drift](https://mlsanity.com/paper/2308.02752/) (2023)
- [Locally-Adaptive Quantization for Streaming Vector Search](https://mlsanity.com/paper/2402.02044/) (2024)
- [Stochastic interior-point methods for smooth conic optimization with applications](https://mlsanity.com/paper/2412.12987/) (2024)
- [Approximation Algorithm for Constrained k-Center Clustering: A Local Search Approach](https://mlsanity.com/paper/2601.11883/) (2026)

## Abstract (arXiv)

Currently the amount of data produced worldwide is increasing beyond measure, thus a high volume of unsupervised data must be processed continuously. One of the main unsupervised data analysis is clustering. In streaming data scenarios, the data is composed by an increasing sequence of batches of samples where the concept drift phenomenon may happen. In this paper, we formally define the Streaming K-means(SKM) problem, which implies a restart of the error function when a concept drift occurs. We propose a surrogate error function that does not rely on concept drift detection. We proof that the surrogate is a good approximation of the SKM error. Hence, we suggest an algorithm which minimizes this alternative error each time a new batch arrives. We present some initialization techniques for streaming data scenarios as well. Besides providing theoretical results, experiments demonstrate an improvement of the converged error for the non-trivial initialization methods.