Automatic Subspace Clustering with Density Function

Jiwu Zhao, Stefan Conrad


Clustering techniques in data mining aim to find interesting patterns in data sets. However, traditional clustering methods are not suitable for large, high-dimensional data. Subspace clustering is an extension of traditional clustering that enables finding clusters in subspaces within a data set, which means subspace clustering is more suitable for detecting clusters in high-dimensional data sets. However, most subspace clustering methods usually require many complicated parameter settings, which are always troublesome to determine, and therefore there are many limitations for applying these subspace clustering methods. In this article, we develop a novel subspace clustering method with a new density function, which computes and represents the density distribution directly in high-dimensional data sets, and furthermore the new method requires as few parameters as possible.


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Paper Citation

in Harvard Style

Zhao J. and Conrad S. (2012). Automatic Subspace Clustering with Density Function . In Proceedings of the International Conference on Data Technologies and Applications - Volume 1: DATA, ISBN 978-989-8565-18-1, pages 63-69. DOI: 10.5220/0004031400630069

in Bibtex Style

author={Jiwu Zhao and Stefan Conrad},
title={Automatic Subspace Clustering with Density Function},
booktitle={Proceedings of the International Conference on Data Technologies and Applications - Volume 1: DATA,},

in EndNote Style

JO - Proceedings of the International Conference on Data Technologies and Applications - Volume 1: DATA,
TI - Automatic Subspace Clustering with Density Function
SN - 978-989-8565-18-1
AU - Zhao J.
AU - Conrad S.
PY - 2012
SP - 63
EP - 69
DO - 10.5220/0004031400630069