An ε-insensitive approach to fuzzy clustering

Jacek Łęski

International Journal of Applied Mathematics and Computer Science (2001)

  • Volume: 11, Issue: 4, page 993-1007
  • ISSN: 1641-876X

Abstract

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Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in the data. The present paper introduces a new ε-insensitive Fuzzy C-Means (εFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMED). The performance of the new clustering algorithm is experimentally compared with the Fuzzy C-Means (FCM) method using synthetic data with outliers and heavy-tailed, overlapped groups of the data.

How to cite

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Łęski, Jacek. "An ε-insensitive approach to fuzzy clustering." International Journal of Applied Mathematics and Computer Science 11.4 (2001): 993-1007. <http://eudml.org/doc/207542>.

@article{Łęski2001,
abstract = {Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in the data. The present paper introduces a new ε-insensitive Fuzzy C-Means (εFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMED). The performance of the new clustering algorithm is experimentally compared with the Fuzzy C-Means (FCM) method using synthetic data with outliers and heavy-tailed, overlapped groups of the data.},
author = {Łęski, Jacek},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {fuzzy c-medians; ε-insensitivity; robust methods; fuzzy c-means; fuzzy clustering; robustness; median-based clustering; fuzzy -means; insensitive objective function; clustering algorithm},
language = {eng},
number = {4},
pages = {993-1007},
title = {An ε-insensitive approach to fuzzy clustering},
url = {http://eudml.org/doc/207542},
volume = {11},
year = {2001},
}

TY - JOUR
AU - Łęski, Jacek
TI - An ε-insensitive approach to fuzzy clustering
JO - International Journal of Applied Mathematics and Computer Science
PY - 2001
VL - 11
IS - 4
SP - 993
EP - 1007
AB - Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in the data. The present paper introduces a new ε-insensitive Fuzzy C-Means (εFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMED). The performance of the new clustering algorithm is experimentally compared with the Fuzzy C-Means (FCM) method using synthetic data with outliers and heavy-tailed, overlapped groups of the data.
LA - eng
KW - fuzzy c-medians; ε-insensitivity; robust methods; fuzzy c-means; fuzzy clustering; robustness; median-based clustering; fuzzy -means; insensitive objective function; clustering algorithm
UR - http://eudml.org/doc/207542
ER -

References

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