@inproceedings{1e8557803ed8480bbd14d581b1519c42,
title = "Identifying projected clusters from gene expression profiles",
abstract = "In microarray gene expression data, clusters may hide in subspaces. Traditional clustering algorithms that make use of similarity measurements in the full input space may fail to detect the clusters. In recent years a number of algorithms have been proposed to identify this kind of projected clusters, but many of them rely on some critical parameters whose proper values are hard for users to determine. In this paper a new algorithm that dynamically adjusts its internal thresholds is proposed. It has a low dependency on user parameters while allowing users to input some domain knowledge should they be available. Experimental results show that the algorithm is capable of identifying some interesting projected clusters from real microarray data.",
author = "Yip, {Kevin Y.} and Cheung, {David W.} and Ng, {Michael K.} and Cheung, {Kei Hoi}",
note = "Funding Information: The research of DWC is supported by a grant from the Research Grant Council of Hong Kong. (Project No.: HKU 7141/03E). K.H.C. is supported in part by NIH Grant K25 HG02378 from the National Human Genome Research Institute and NSF Grant DBI-0135442. Publisher copyright: {\textcopyright} 2004 IEEE; Proceedings - Fourth IEEE Symposium on Bioinformatics and Bioengineering, BIBE 2004 ; Conference date: 19-05-2004 Through 21-05-2004",
year = "2004",
month = may,
day = "21",
doi = "10.1109/BIBE.2004.1317352",
language = "English",
isbn = "0769521738",
series = "Proceedings - IEEE Symposium on Bioinformatics and Bioengineering, BIBE",
publisher = "IEEE",
pages = "259--266",
editor = "Deeber Azada",
booktitle = "Proceedings - Fourth IEEE Symposium on Bioinformatics and Bioengineering, BIBE 2004",
address = "United States",
}