# Improved Clustering with Augmented k-means

arXiv: https://arxiv.org/abs/1705.07592 · HTML: https://mlsanity.com/paper/1705.07592/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2017.

## Built on by (most cited first, 1 of 1)

- [Learning-Augmented k-means Clustering](https://mlsanity.com/paper/2110.14094/) (2021)

## Abstract (arXiv)

Identifying a set of homogeneous clusters in a heterogeneous dataset is one of the most important classes of problems in statistical modeling. In the realm of unsupervised partitional clustering, k-means is a very important algorithm for this. In this technical report, we develop a new k-means variant called Augmented k-means, which is a hybrid of k-means and logistic regression. During each iteration, logistic regression is used to predict the current cluster labels, and the cluster belonging probabilities are used to control the subsequent re-estimation of cluster means. Observations which can't be firmly identified into clusters are excluded from the re-estimation step. This can be valuable when the data exhibit many characteristics of real datasets such as heterogeneity, non-sphericity, substantial overlap, and high scatter. Augmented k-means frequently outperforms k-means by more accurately classifying observations into known clusters and / or converging in fewer iterations. We demonstrate this on both simulated and real datasets. Our algorithm is implemented in Python and will be available with this report.