@inproceedings{1f8f3eaa95e148f2aab06dcc7e04b371,
title = "A novel alternative exponent-weighted fuzzy C-means algorithm",
abstract = "Under noisy environment and uneven data distribution, Fuzzy C-Means (FCM) algorithm and some of its advanced algorithms give large miss-clustering result or become malfunction. This paper proposes a novel Alternative Exponent-weighted Fuzzy C-Means (AEFCM) algorithm which introduces exponent-weight matrix and defines a new metric space. During iteration, the exponent-weight matrix gives every data sample a difference weight based on difference cluster center. Meanwhile, new metric space can efficiently restrain the bad influence produced by noisy samples during the iteration. Experiments have proved that AEFCM algorithm may overcome the bugs of FCM algorithm in a certain extent, with favorable convergence and robustness.",
keywords = "AEFCM, Exponent-weighted, FCM, Fuzzy clustering, Metric space",
author = "Renhao Fan and Xiang Wang and Jordi Madrenas",
year = "2013",
doi = "10.1109/GreenCom-iThings-CPSCom.2013.325",
language = "英语",
isbn = "9780769550466",
series = "Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013",
pages = "1767--1772",
booktitle = "Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013",
note = "2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013 ; Conference date: 20-08-2013 Through 23-08-2013",
}