@inproceedings{dad4b47a9ee840648ca4cb468eea03e8,
title = "Clustering Big Cancer Data by Effect Sizes",
abstract = "We propose an effect size based approach to compute initial dissimilarities for Ensemble Algorithm of Clustering Cancer Data (EACCD). The proposed method is applied to the colon cancer data from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute and compared with the log-rank approach where initial dissimilarities are computed from the log-rank test statistic. The experimental results show that under the proportional hazards assumption, the effect size approach generates robust results and has a better performance than the log-rank approach.",
keywords = "TNM, colon cancer, dendrogram, hierarchical clustering, prognostic system, survival",
author = "Huan Wang and Dechang Chen and Hueman, {Matthew T.} and Li Sheng and Henson, {Donald E.}",
note = "Funding Information: ACKNOWLEDGMENT This work was supported by the grant “Using Dendrograms to Create Prognostic Systems for Cancer sponsored by John P. Murtha Cancer Center Research Program.” Disclaimer: The views expressed are those of the authors and do not necessarily reflect the official views of the Uniformed Services University of the Health Sciences, the Department of Defense, or the U.S. Government. Publisher Copyright: {\textcopyright} 2017 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 2nd IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2017 ; Conference date: 17-07-2017 Through 19-07-2017",
year = "2017",
month = aug,
day = "14",
doi = "10.1109/CHASE.2017.60",
language = "English (US)",
series = "Proceedings - 2017 IEEE 2nd International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "58--63",
booktitle = "Proceedings - 2017 IEEE 2nd International Conference on Connected Health",
}