8/19/2019 Find Cluster Centers With Subtractive Clustering -
MATLAB Subclust
2/2
Examples
[C,S] = subclust(X,0.5)
This command sets the minimum number of arguments needed to use
this function. A range of influence of 0.5has been specified for
all data dimensions.
[C,S] = subclust(X,[0.5 0.25 0.3],[],[2.0 0.8 0.7])
This command assumes the data dimension is 3 ( X has 3 columns)
and uses a range of influence of 0.5 , 0.25 ,and 0.3 for the first,
second, and third data dimension, respectively. The scaling factors
for mapping the datainto a unit hyperbox are obtained from the
minimum and maximum data values. The squashFactor is set to 2.0
,indicating that you only want to find clusters that are far from
each other. The acceptRatio is set to 0.8 ,indicating that only
data points that have a very strong potential for being cluster
centers are accepted.The rejectRatio is set to 0.7 , indicating
that you want to reject all data points without a strong
potential.
Related Examples
Model Suburban Commuting Using Subtractive Clustering
More About
Fuzzy Clustering
References
Chiu, S., "Fuzzy Model Identification Based on Cluster
Estimation," Journal of Intelligent & Fuzzy Systems , Vol.2,
No. 3, Sept. 1994.
Yager, R. and D. Filev, "Generation of Fuzzy Rules by Mountain
Clustering," Journal of Intelligent & Fuzzy
Systems , Vol. 2, No. 3, pp. 209-219, 1994.
See Also
genfis2
http://www.mathworks.com/help/fuzzy/genfis2.htmlhttp://www.mathworks.com/help/fuzzy/fuzzy-clustering.htmlhttp://www.mathworks.com/help/fuzzy/model-suburban-commuting-using-subtractive-clustering.html
