Fuzzy c-means (FCM) clustering algorithm is sensitive to noise points and outlier data, and the possibilistic
fuzzy c-means (PFCM) clustering algorithm overcomes the problem well, but PFCM clustering algorithm
has some problems: it is still sensitive to initial clustering centers and the clustering results are not good when
the tested datasets with noise are very unequal. An improved kernel possibilistic fuzzy c-means algorithm based
on invasive weed optimization (IWO-KPFCM) is proposed in this paper. This algorithm first uses invasive weed
optimization (IWO) algorithm to seek the optimal solution as the initial clustering centers, and introduces kernel
method to make the input data from the sample space map into the high-dimensional feature space. Then, the
sample variance is introduced in the objection function to measure the compact degree of data. Finally, the improved
algorithm is used to cluster data. The simulation results of the University of California-Irvine (UCI) data
sets and artificial data sets show that the proposed algorithm has stronger ability to resist noise, higher cluster
accuracy and faster convergence speed than the PFCM algorithm.
ZHAO Xiao-qiang* (赵小强), ZHOU Jin-hu (周金虎)
. Improved Kernel Possibilistic Fuzzy Clustering Algorithm Based on Invasive Weed Optimization[J]. Journal of Shanghai Jiaotong University(Science), 2015
, 20(2)
: 164
-170
.
DOI: 10.1007/s12204-015-1605-z
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