# Experimental Estimation of Number of Clusters Based on Cluster Quality

arXiv: https://arxiv.org/abs/1503.03168 · HTML: https://mlsanity.com/paper/1503.03168/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2015.

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

- [Are Easy Data Easy (for K-Means)](https://mlsanity.com/paper/2308.01926/) (2023)

## Abstract (arXiv)

Text Clustering is a text mining technique which divides the given set of text documents into significant clusters. It is used for organizing a huge number of text documents into a well-organized form. In the majority of the clustering algorithms, the number of clusters must be specified apriori, which is a drawback of these algorithms. The aim of this paper is to show experimentally how to determine the number of clusters based on cluster quality. Since partitional clustering algorithms are well-suited for clustering large document datasets, we have confined our analysis to a partitional clustering algorithm.