1 research outputs found
A Kernel-Based Membrane Clustering Algorithm
The existing membrane clustering algorithms may fail to
handle the data sets with non-spherical cluster boundaries. To overcome
the shortcoming, this paper introduces kernel methods into membrane
clustering algorithms and proposes a kernel-based membrane clustering
algorithm, KMCA. By using non-linear kernel function, samples in
original data space are mapped to data points in a high-dimension feature
space, and the data points are clustered by membrane clustering
algorithms. Therefore, a data clustering problem is formalized as a kernel
clustering problem. In KMCA algorithm, a tissue-like P system is
designed to determine the optimal cluster centers for the kernel clustering
problem. Due to the use of non-linear kernel function, the proposed
KMCA algorithm can well deal with the data sets with non-spherical
cluster boundaries. The proposed KMCA algorithm is evaluated on nine
benchmark data sets and is compared with four existing clustering algorithms