Informatics Publishing Limited

D. Sharmila Rani ¹, N. Kousika ², G. Komarasamy ³

Affiliations
1 Sri Krishna College of Engineering and Technology, Coimbatore, IN
2 Department of Computer Science, Sri Krishna College of Engineering and Technology, Coimbatore, IN
3 Computer Science and Engineering Department, Bannari Amman Institute of Technology, IN

Abstract

Clustering is an unsupervised classification that is the partitioning of a data set in a set of meaningful subsets. Each object in dataset shares some common property often proximity according to some defined distance measure. Among various types of clustering techniques, K-Means is one of the most popular algorithms. The objective of K-means algorithm is to make the distances of objects in the same cluster as small as possible. Algorithms, systems and frameworks that address clustering challenges have been more elaborated over the past years. In this review paper, we present the K-Means algorithm and its improved techniques.

Keywords

Classification, Clustering, K-Means Clustering, Partitioning Clustering.

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K. Venkateswaran ¹, T. Sowmya Shree ¹, N. Kousika ¹, N. Kasthuri ¹

Affiliations
1 Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, IN

Abstract

Background/Objectives: - Feature Selection is applicable to decrease the number of features in various applications wherein the data include hundreds and thousands of features. The objective of this study is to choose Genetic Algorithm for feature selection to obtain better fitness function. Methods/Statistical Analysis: Particle Swarm Optimization (PSO) approach is used for selecting the subset from the combination of texture based features and providing the better fitness values. In this paper PSO is used to obtain the feature sets and the performance is compared with genetic algorithm. Support vector machine classifier is used to improve the classification accuracy. Findings: The experimental results shows that PSO overall accuracy is improved to LISS IV 1.7%, 1.4% and 2.9% and the kappa coefficient is improved to 0.06%, 0.012% and 0.39% as compared to GA. Application/Improvements: The Fitness value obtained by GA is more complex and not accurate. To reduce the complexity and increase the accuracy Particle Swarm optimization is used. Hence PSO improved the quality of texture based images.

Keywords

Artificial Neural Networks, Feature Extraction, Genetic Algorithm, Particle Swarm Optimization, Support Vector Machine

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