---
title: "Distributed Clustering Based on Sampling Local Density Estimates"
authors:
  - "Matthias Klusch"
  - "Stefano Lodi"
  - "Gianluca Moro"
doi: ""
url: "https://ijcai.org/proceedings/2003/72"
pdf: "https://ijcai.org/proceedings/2003/0072.pdf"
venue: "Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence"
year: 2003
pages: "485-489"
track: ""
keywords:
---

# Distributed Clustering Based on Sampling Local Density Estimates

Matthias Klusch, Stefano Lodi, Gianluca Moro

*Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence*, pages 485-489.

## Abstract

Huge amounts of data are stored in autonomous, geographically distributed sources. The discovery of previously unknown, implicit and valuable knowledge is a key aspect of the exploitation of such sources. In recent years several approaches to knowledge discovery and data mining, and in particular to clustering, have been developed, but only a few of them are designed for distributed data sources. We propose a novel distributed clustering algorithm based on non-parametric kernel density estimation, which takes into account the issues of privacy and communication costs that arise in a distributed environment.