Hybrid-K-means-Pso(MATLAB)
An advanced version of K-Means using Particle swarm optimization for clustering of high dimensional data sets, which converges faster to the optimal solution.
“Clustering” is a technique that is employed to partition elements in a data set such that similar elements are assigned to same cluster while elements with different properties are assigned to different clusters. Fast and high-quality document clustering algorithms play an important role in effectively navigating, summarizing, and organizing information. Partitional clustering algorithms are more suitable for clustering large datasets. In this project we are going to implement a hybrid Particle Swarm Optimization (PSO) with K-means document clustering algorithm that performs fast document clustering and can avoid being trapped in a local optimal solution on various high dimensional datasets. The hybrid PSO with K-means algorithm combines the ability of the globalized searching of the PSO algorithm and the fast convergence of the K- means algorithm. The results obtained are analyzed and compared for accuracy and performance of the algorithm on large datasets.
Data sets: IRIS, Poker, Heart, Contraceptive Method Choice Data Set (Taken from UCI repository)
Copy all the gui files if you also want gui along with the code. Else just copy the respective Kmeans.m, KPSO.m and KPSOK.m files of the data of your requirement and execute them in the same order. All the data files are also included.
The entire information, background and results are present in the doc2.pdf in detail.
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