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Bio Inspired Algorithms for Dimensionality Reduction and Outlier Detection in Medical Datasets


Affiliations
1 Department of Computer Science, School of Computer Science and Engineering, Bharathiar University, Coimbatore, India
2 Department of Computer Science, PSG College of Arts & Science, Coimbatore, India
3 Department of Computer Science, Nirmala College for Women, Coimbatore, India
 

Dimensionality Reduction is one of the useful techniques used in number of applications in order to reduce the number of features to improve the productivity and efficiency of the task. Clustering is one of the influential tasks in data mining. Dimensionality reductions are used in data mining, Image processing, Networking, Mobile computing, etc. The elementary intention of this work is to apply dimensionality reduction algorithms and then cluster the datasets to detect outliers. A bio-inspired ACO (Ant Colony optimization) algorithm has been proposed to reduce dimensionality. Also another bio-inspired algorithm FA (Firefly Algorithm) has been proposed to detect outliers. The three distinct medical datasets: thyroid dataset, Oesophagal dataset and Heart disease dataset are used for experimental results.

Keywords

Dimensionality Reduction, Clustering, Outlier Detection, ACO (Ant Colony Optimization) Algorithm, FA (Firefly Algorithm).
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  • Bio Inspired Algorithms for Dimensionality Reduction and Outlier Detection in Medical Datasets

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Authors

S. Vijayarani
Department of Computer Science, School of Computer Science and Engineering, Bharathiar University, Coimbatore, India
C. Sivamathi
Department of Computer Science, PSG College of Arts & Science, Coimbatore, India
S. Maria Sylviaa
Department of Computer Science, Nirmala College for Women, Coimbatore, India

Abstract


Dimensionality Reduction is one of the useful techniques used in number of applications in order to reduce the number of features to improve the productivity and efficiency of the task. Clustering is one of the influential tasks in data mining. Dimensionality reductions are used in data mining, Image processing, Networking, Mobile computing, etc. The elementary intention of this work is to apply dimensionality reduction algorithms and then cluster the datasets to detect outliers. A bio-inspired ACO (Ant Colony optimization) algorithm has been proposed to reduce dimensionality. Also another bio-inspired algorithm FA (Firefly Algorithm) has been proposed to detect outliers. The three distinct medical datasets: thyroid dataset, Oesophagal dataset and Heart disease dataset are used for experimental results.

Keywords


Dimensionality Reduction, Clustering, Outlier Detection, ACO (Ant Colony Optimization) Algorithm, FA (Firefly Algorithm).

References