Subspace Clustering with Distance-density Function and Entropy in High-dimensional Data

Jiwu Zhao, Stefan Conrad

Abstract

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 almost troublesome to determine, and therefore there are many limitations in applying these subspace clustering methods. In our previous work, we developed a subspace clustering method Automatic Subspace Clustering with Distance-Density function (ASCDD), which computes the density distribution directly in high-dimensional data sets by using just one parameter. In order to facilitate choosing the parameter in ASCDD we analyze the relation of neighborhood objects and investigate a new way of determining the range of the parameter in this article. Furthermore, we will introduce here a new method by applying entropy in detecting potential subspaces in ASCDD, which evidently reduces the complexity of detecting relevant subspaces.

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Paper Citation


in Harvard Style

Zhao J. and Conrad S. (2013). Subspace Clustering with Distance-density Function and Entropy in High-dimensional Data . In Proceedings of the 2nd International Conference on Data Technologies and Applications - Volume 1: DATA, ISBN 978-989-8565-67-9, pages 14-22. DOI: 10.5220/0004486600140022


in Bibtex Style

@conference{data13,
author={Jiwu Zhao and Stefan Conrad},
title={Subspace Clustering with Distance-density Function and Entropy in High-dimensional Data},
booktitle={Proceedings of the 2nd International Conference on Data Technologies and Applications - Volume 1: DATA,},
year={2013},
pages={14-22},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004486600140022},
isbn={978-989-8565-67-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Data Technologies and Applications - Volume 1: DATA,
TI - Subspace Clustering with Distance-density Function and Entropy in High-dimensional Data
SN - 978-989-8565-67-9
AU - Zhao J.
AU - Conrad S.
PY - 2013
SP - 14
EP - 22
DO - 10.5220/0004486600140022