Gaussian kernel particle swarm optimization clustering algorithm

As the K-means algorithm is dependent on the initial clustering center, and the particle swarm optimization (PSO) converges prematurely and is easily trapped in local minima, a Gaussian kernel particle swarm optimization clustering algorithm is proposed in this paper. The algorithm adopts the theory of good point set to initialize population, which makes the initial clustering center more rational. Particle swarm iteration formula was optimized by using Gaussian kernel method, which makes particle swarm algorithm converge rapidly to the global optimal. By testing 23 UCI data sets, the experimental results show that the clustering effect of the proposed algorithm is better than that of the K-means and the traditional particle swarm optimization clustering algorithm.

[1]  Z Li,et al.  KERNEL CLUSTERING ALGORITHM , 2002 .

[2]  Ujjwal Maulik,et al.  Genetic algorithm-based clustering technique , 2000, Pattern Recognit..

[3]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2006 .

[4]  George Karypis,et al.  Hierarchical Clustering Algorithms for Document Datasets , 2005, Data Mining and Knowledge Discovery.

[5]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[6]  Daewon Lee,et al.  Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Bing Liu,et al.  An efficient semi-unsupervised gene selection method via spectral biclustering , 2006, IEEE Transactions on NanoBioscience.

[8]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[9]  Lipo Wang,et al.  Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[10]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[11]  Michalis Vazirgiannis,et al.  c ○ 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. On Clustering Validation Techniques , 2022 .

[12]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[13]  M. Narasimha Murty,et al.  Genetic K-means algorithm , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[14]  Xu Zhou,et al.  Parallel hybrid PSO with CUDA for lD heat conduction equation , 2015 .

[15]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[16]  Daewon Lee,et al.  An improved cluster labeling method for support vector clustering , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Sean Hughes,et al.  Clustering by Fast Search and Find of Density Peaks , 2016 .

[18]  Lipo Wang,et al.  On competitive learning , 1997, IEEE Trans. Neural Networks.

[19]  V.S. Tseng,et al.  Efficiently mining gene expression data via a novel parameterless clustering method , 2005, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[20]  Alessandro Laio,et al.  Clustering by fast search and find of density peaks , 2014, Science.

[21]  Lipo Wang,et al.  Data Mining With Computational Intelligence , 2006, IEEE Transactions on Neural Networks.

[22]  Zhang Ling,et al.  Good Point Set Based Genetic Algorithm , 2001 .

[23]  Jun Tang,et al.  Particle Swarm Optimization with Adaptive Mutation , 2009, 2009 WASE International Conference on Information Engineering.

[24]  Hou Zhi-rong,et al.  Particle Swarm Optimization with Adaptive Mutation , 2006 .

[25]  Jing Xu,et al.  Improved Cluster Algorithm Based on K-Means and Particle Swarm Optimization: Improved Cluster Algorithm Based on K-Means and Particle Swarm Optimization , 2010 .

[26]  Kenli Li,et al.  Hybrid particle swarm optimization for parameter estimation of Muskingum model , 2014, Neural Computing and Applications.