# Effect of Different Distance Measures on the Performance of K-Means Algorithm: An Experimental Study in Matlab

arXiv: https://arxiv.org/abs/1405.7471 · HTML: https://mlsanity.com/paper/1405.7471/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2014.

## Built on by (most cited first, 3 of 3)

- [Robustness-Reinforced Knowledge Distillation with Correlation Distance and Network Pruning](https://mlsanity.com/paper/2311.13934/) (2023)
- [Genetic algorithm for feature selection of EEG heterogeneous data](https://mlsanity.com/paper/2103.07117/) (2021)
- [Applied Machine Learning to Anomaly Detection in Enterprise Purchase Processes](https://mlsanity.com/paper/2405.14754/) (2024)

## Abstract (arXiv)

K-means algorithm is a very popular clustering algorithm which is famous for its simplicity. Distance measure plays a very important rule on the performance of this algorithm. We have different distance measure techniques available. But choosing a proper technique for distance calculation is totally dependent on the type of the data that we are going to cluster. In this paper an experimental study is done in Matlab to cluster the iris and wine data sets with different distance measures and thereby observing the variation of the performances shown.