@inproceedings{37529a513e034fafb8bdbbb841657294,
title = "Fuzzy clustering model of data with proportional membership",
abstract = "The fuzzy clustering proportional membership (FCPM) proposes a model of how data are generated from a cluster structure to be identified. Clusters' prototypes and membership function are meaningful in the context of the model. In particular, the membership of an entity to a cluster expresses the proportion of the cluster's prototype reflected in the entity (proportional membership). In this work we explore the notion of proportional membership and compare it against the fuzzy c-means (FCM) distance membership. The ability of FCPM to reveal the underlying clustering model of data has been studied and a comparison with FCM had been performed as well.",
author = "S. Nascimento and B. Mirkin and F. Moura-Pires",
year = "2000",
month = jan,
day = "1",
doi = "10.1109/NAFIPS.2000.877433",
language = "English",
isbn = "0-7803-6274-8",
series = "Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "261--266",
editor = "T. Whalen",
booktitle = "PEACHFUZZ 2000: 19TH INTERNATIONAL CONFERENCE OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY - NAFIPS",
address = "United States",
note = "19th International Confernce of the North American Fuzzy Information Processing Society-NAFIPS (PEACH FUZZ 2000) ; Conference date: 13-07-2000 Through 15-07-2000",
}