# Clustering with Neural Network and Index

arXiv: https://arxiv.org/abs/2212.03853 · HTML: https://mlsanity.com/paper/2212.03853/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [Coordinate Descent Algorithms](https://mlsanity.com/paper/1502.04759/) (2015)
- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions](https://mlsanity.com/paper/2206.07579/) (2022)
- [Clustering with Deep Learning: Taxonomy and New Methods](https://mlsanity.com/paper/1801.07648/) (2018)
- [A New Index for Clustering Evaluation Based on Density Estimation](https://mlsanity.com/paper/2207.01294/) (2022)
- [Topic Model Supervised by Understanding Map](https://mlsanity.com/paper/2110.06043/) (2021)

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

- [Min-Max-Jump distance and its applications](https://mlsanity.com/paper/2301.05994/) (2023)

## Abstract (arXiv)

A new model called Clustering with Neural Network and Index (CNNI) is introduced. CNNI uses a Neural Network to cluster data points. Training of the Neural Network mimics supervised learning, with an internal clustering evaluation index acting as the loss function. An experiment is conducted to test the feasibility of the new model, and compared with results of other clustering models like K-means and Gaussian Mixture Model (GMM). The result shows CNNI can work properly for clustering data; CNNI equipped with MMJ-SC, achieves the first parametric (inductive) clustering model that can deal with non-convex shaped (non-flat geometry) data.