scholarly journals Enhance Clustering Algorithm Using Optimization

2020 ◽  
Vol 3 (9) ◽  
pp. 136-142
Author(s):  
Roshankumar Ramashish Maurya ◽  
Anand Khandare
Keyword(s):  
Particle Swarm Optimization ◽  
Unsupervised Learning ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Unsupervised Clustering ◽  
Classification Problems ◽  
Swarm Optimization ◽  
Different Types

Unsupervised learning can reveal the structure of datasets without being concerned with any labels, K-means clustering is one such method. Traditionally the initial clusters have been selected randomly, with the idea that the algorithm will generate better clusters. However, studies have shown there are methods to improve this initial clustering as well as the K-means process. This paper examines these results on different types of datasets to study if these results hold for all types of data. Another method that is used for unsupervised clustering is the algorithm based on Particle Swarm Optimization. For the second part this paper studies the classic K-means based algorithm and a Hybrid K-means algorithm which uses PSO to improve the results from K-means. The hybrid K-means algorithms are compared to the standard K-means clustering on two benchmark classification problems. In this project we used Kaggle dataset to with different size (small, large and medium) for comparison PSO, k-means and k-means hybrid.

scholarly journals A Constructive Data Classification Version of the Particle Swarm Optimization Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Alexandre Szabo ◽  
Leandro Nunes de Castro
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Classification Problems ◽  
Swarm Optimization ◽  
Swarm Algorithms ◽  
Wide Range

The particle swarm optimization algorithm was originally introduced to solve continuous parameter optimization problems. It was soon modified to solve other types of optimization tasks and also to be applied to data analysis. In the latter case, however, there are few works in the literature that deal with the problem of dynamically building the architecture of the system. This paper introduces new particle swarm algorithms specifically designed to solve classification problems. The first proposal, named Particle Swarm Classifier (PSClass), is a derivation of a particle swarm clustering algorithm and its architecture, as in most classifiers, is pre-defined. The second proposal, named Constructive Particle Swarm Classifier (cPSClass), uses ideas from the immune system to automatically build the swarm. A sensitivity analysis of the growing procedure of cPSClass and an investigation into a proposed pruning procedure for this algorithm are performed. The proposals were applied to a wide range of databases from the literature and the results show that they are competitive in relation to other approaches, with the advantage of having a dynamically constructed architecture.

scholarly journals A Novel Weighted Support Vector Machine Based on Particle Swarm Optimization for Gene Selection and Tumor Classification

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Mohammad Javad Abdi ◽  
Seyed Mohammad Hosseini ◽  
Mansoor Rezghi
Keyword(s):  
Particle Swarm Optimization ◽  
Dna Microarrays ◽  
Gene Selection ◽  
Particle Swarm ◽  
Tumor Classification ◽  
Support Vector ◽  
Classification Problems ◽  
Target Class ◽  
Swarm Optimization ◽  
Kernel Parameters

We develop a detection model based on support vector machines (SVMs) and particle swarm optimization (PSO) for gene selection and tumor classification problems. The proposed model consists of two stages: first, the well-known minimum redundancy-maximum relevance (mRMR) method is applied to preselect genes that have the highest relevance with the target class and are maximally dissimilar to each other. Then, PSO is proposed to form a novel weighted SVM (WSVM) to classify samples. In this WSVM, PSO not only discards redundant genes, but also especially takes into account the degree of importance of each gene and assigns diverse weights to the different genes. We also use PSO to find appropriate kernel parameters since the choice of gene weights influences the optimal kernel parameters and vice versa. Experimental results show that the proposed mRMR-PSO-WSVM model achieves highest classification accuracy on two popular leukemia and colon gene expression datasets obtained from DNA microarrays. Therefore, we can conclude that our proposed method is very promising compared to the previously reported results.

scholarly journals Memetic Variable Clustering and Its Application

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
JiaCheng Ni ◽  
Li Li
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Experimental Result ◽  
Swarm Optimization ◽  
Metaheuristic Optimization ◽  
Variable Clustering

Clustering analysis is an important and difficult task in data mining and big data analysis. Although being a widely used clustering analysis technique, variable clustering did not get enough attention in previous studies. Inspired by the metaheuristic optimization techniques developed for clustering data items, we try to overcome the main shortcoming of k-means-based variable clustering algorithm, which is being sensitive to initial centroids by introducing the metaheuristic optimization. A novel memetic algorithm named MCLPSO (Memetic Comprehensive Learning Particle Swarm Optimization) based on CLPSO (Comprehensive Learning Particle Swarm Optimization) has been studied under the framework of memetic computing in our previous work. In this work, MCLPSO is used as a metaheuristic approach to improve the k-means-based variable clustering algorithm by adjusting the initial centroids iteratively to maximize the homogeneity of the clustering results. In MCLPSO, a chaotic local search operator is used and a simulated annealing- (SA-) based local search strategy is developed by combining the cognition-only PSO model with SA. The adaptive memetic strategy can enable the stagnant particles which cannot be improved by the comprehensive learning strategy to escape from the local optima and enable some elite particles to give fine-grained local search around the promising regions. The experimental result demonstrates a good performance of MCLPSO in optimizing the variable clustering criterion on several datasets compared with the original variable clustering method. Finally, for practical use, we also developed a web-based interactive software platform for the proposed approach and give a practical case study—analyzing the performance of semiconductor manufacturing system to demonstrate the usage.

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