Experimental Study: Comparison of clustering algorithms
Journal Title: International Journal of engineering Research and Applications - Year 2017, Vol 7, Issue 8
Abstract
One of the most important processes in the machine learning is the clustering. The clustering is an unsupervised process that gathers all similar measurements to identify and put them in groups based on specific measurements. Clustering task is required in many applications such as, text analysis, data visualization, nature language processing, image processing, computer vision, and even gene expression analysis. This work tends to make a comparison study to analyze the performance of different clustering algorithms using different datasets. We conduct some experimental results to evaluate the effectiveness of six clustering algorithms: hard K mean, fuzzy K mean, Locality weighted of hard K mean, Locality weighted of fuzzy K mean, Hierarchical , and DBSCAN algorithms. We use synaptic and real dataset in our experiments. We synthesize three different datasets to analyze the performance: imbalanced classes dataset, an outlier dataset, and moon dataset. Additionally, we perform image segmentation and compression using these clustering algorithms. Finally, we test the performance of the algorithms by performing facial expression clustering, which is one of the most challenging problem in the computer vision.
Authors and Affiliations
Mohammed Dawod, Mays Hasan, Amar Daood
Keywords
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- EP ID EP392042
- DOI 10.9790/9622-0708042334.
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