# CNAK : Cluster Number Assisted K-means

arXiv: https://arxiv.org/abs/1911.08871 · HTML: https://mlsanity.com/paper/1911.08871/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

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

- [CNMBI: Determining the Number of Clusters Using Center Pairwise Matching and Boundary Filtering](https://mlsanity.com/paper/2603.26744/) (2026)
- [IPD:An Incremental Prototype based DBSCAN for large-scale data with cluster representatives](https://mlsanity.com/paper/2202.07870/) (2022)
- [QABBA: Symbolic Time-Series Compression via Integer-Quantized Aggregation](https://mlsanity.com/paper/2411.15209/) (2024)
- [Absolute indices for determining compactness, separability and number of clusters](https://mlsanity.com/paper/2510.13065/) (2025)

## Abstract (arXiv)

Determining the number of clusters present in a dataset is an important problem in cluster analysis. Conventional clustering techniques generally assume this parameter to be provided up front. %user supplied. %Recently, robustness of any given clustering algorithm is analyzed to measure cluster stability/instability which in turn determines the cluster number. In this paper, we propose a method which analyzes cluster stability for predicting the cluster number. Under the same computational framework, the technique also finds representatives of the clusters. The method is apt for handling big data, as we design the algorithm using Monte-Carlo simulation. Also, we explore a few pertinent issues found to be of also clustering. Experiments reveal that the proposed method is capable of identifying a single cluster. It is robust in handling high dimensional dataset and performs reasonably well over datasets having cluster imbalance. Moreover, it can indicate cluster hierarchy, if present. Overall we have observed significant improvement in speed and quality for predicting cluster numbers as well as the composition of clusters in a large dataset.