---
title: "Data Clustering: Principal Components, Hopfield and Self-Aggregation Networks"
authors:
  - "Chris H. Q. Ding"
doi: ""
url: "https://ijcai.org/proceedings/2003/71"
pdf: "https://ijcai.org/proceedings/2003/0071.pdf"
venue: "Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence"
year: 2003
pages: "479-483"
track: ""
keywords:
---

# Data Clustering: Principal Components, Hopfield and Self-Aggregation Networks

Chris H. Q. Ding

*Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence*, pages 479-483.

## Abstract

We present a coherent framework for data clustering~ Starting with a Hopfield network, we show the solutions for several well-motivated clustering objective functions are principal components. For MinMaxCut objectives motivated for ensuring cluster balance, the solutions are the nonlinearly scaled principal components. Using scaled PCA, we generalize to multi-way clustering, constructing a self-aggregation network, where connection weights between different clusters are automatically suppressed while connection weights within same clusters are automatically enhanced.