Distributed Clustering Based on Sampling Local Density Estimates
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.
@inproceedings{ijcai2003p72,
title = {Distributed Clustering Based on Sampling Local Density Estimates},
author = {Klusch, Matthias and Lodi, Stefano and Moro, Gianluca},
booktitle = {Proceedings of the Eighteenth International Joint Conference on
Artificial Intelligence, {IJCAI-03}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Georg Gottlob},
pages = {485--489},
year = {2003},
url = {https://ijcai.org/proceedings/2003/72},
}
TY - CPAPER TI - Distributed Clustering Based on Sampling Local Density Estimates AU - Klusch, Matthias AU - Lodi, Stefano AU - Moro, Gianluca T2 - Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03 PB - International Joint Conferences on Artificial Intelligence PY - 2003 SP - 485 EP - 489 UR - https://ijcai.org/proceedings/2003/72 ER -Copied
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