@article{id:Siminski2017Fuzzy,
title = "Fuzzy weighted C-ordered means clustering algorithm ",
journal = "Fuzzy Sets and Systems ",
volume = {318},
pages = {1-33},
year = "2017",
issn = "0165-0114",
doi = "http://dx.doi.org/10.1016/j.fss.2017.01.001",
url = "http://www.sciencedirect.com/science/article/pii/S0165011417300180",
author = "Krzysztof Siminski",
abstract = "Abstract In real life data sets some attributes may have 
lower importance or even may be completely noninformative. The subspace 
clustering algorithms have been proposed to handle this. The soft 
subspace algorithms are vulnerable to noise and outliers. The paper 
presents a novel algorithm that handles both various importance of 
attributes and outliers. The proposed Fuzzy Weighted C-Ordered Mean 
(FWCOM) clustering algorithm elaborates clusters in soft subspaces. 
In each cluster each attribute is assigned a weight from interval [0, 1]. 
Each attribute has its individual weight (importance) in each cluster. 
The proposed algorithm applies the ordering technique to effectively 
reduce the influence of outliers and noise. The paper is accompanied 
by numerical experiments. "
}