|
Title:
|
A NEW FEATURE WEIGHTED FUZZY C-MEANS CLUSTERING ALGORITHM |
|
Author(s):
|
Huaiguo Fu , Ahmed M. Elmisery |
|
ISBN:
|
978-972-8924-88-1 |
|
Editors:
|
Ajith P. Abraham |
|
Year:
|
2009 |
|
Edition:
|
Single |
|
Keywords:
|
Cluster Analysis, Fuzzy Clustering, Feature Weighted. |
|
Type:
|
Full Paper |
|
First Page:
|
11 |
|
Last Page:
|
18 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
In the field of cluster analysis, most of existing algorithms assume that each feature of the samples plays a uniform
contribution for cluster analysis. Feature-weight assignment is a special case of feature selection where different features
are ranked according to their importance. The feature is assigned a value in the interval [0, 1] indicating the importance of
that feature, we call this value "feature-weight". In this paper we propose a new feature weighted fuzzy c-means
clustering algorithm in a way which this algorithm be able to obtain the importance of each feature, and then use it in
appropriate assignment of feature-weight. These weights incorporated into the distance measure to shape clusters based
on variability, correlation and weighted features. |
|
|
|
|
|
|
