# Document Clustering using K-Means and K-Medoids

arXiv: https://arxiv.org/abs/1502.07938 · HTML: https://mlsanity.com/paper/1502.07938/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2015.

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

- [DocSCAN: Unsupervised Text Classification via Learning from Neighbors](https://mlsanity.com/paper/2105.04024/) (2021)

## Abstract (arXiv)

With the huge upsurge of information in day-to-days life, it has become difficult to assemble relevant information in nick of time. But people, always are in dearth of time, they need everything quick. Hence clustering was introduced to gather the relevant information in a cluster. There are several algorithms for clustering information out of which in this paper, we accomplish K-means and K-Medoids clustering algorithm and a comparison is carried out to find which algorithm is best for clustering. On the best clusters formed, document summarization is executed based on sentence weight to focus on key point of the whole document, which makes it easier for people to ascertain the information they want and thus read only those documents which is relevant in their point of view.