@inproceedings{b2ef6a8719154416b69532898c0d79f6,
title = "FTCluster: Efficient mining fault-tolerant biclusters in microarray dataset",
abstract = "Biclustering is a popular method for microarray dataset analysis. It allows for condition set and gene set points clustering simultaneously. However, the noisy data in microarray may disturb the mining results. In order to reduce the influence of noise and find more biological biclusters, we propose an algorithm, FTCluster, to mine fault-tolerant biclusters in microarray dataset. Unlike traditional fault-tolerant biclusters mining algorithms, FTCluster uses several novel techniques to improve the efficiency. It also adopts several techniques to generate relaxed biclusters without candidate maintenance. The experimental results show FTCluster is more effective than traditional algorithms. The biological significance of FTCluster is evaluated by Gene Ontology and the results show FTCluster can find larger biological relevant biclusters.",
keywords = "Biclustering, Fault-tolerant bicluster, Gene expression, Microarray",
author = "Miao Wang and Xuequn Shang and Miao Miao and Zhanhuai Li and Wenbin Liu",
year = "2011",
doi = "10.1109/ICDMW.2011.89",
language = "英语",
isbn = "9780769544090",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
pages = "1075--1082",
booktitle = "Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011",
note = "11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 ; Conference date: 11-12-2011 Through 11-12-2011",
}