Comparitive study on the performance of various clustering approaches
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Abstract
Clustering is a technique of discovering and grouping similar objects into a single logical unit. Clusters can be created based on similarities or dissimilarities between objects. Usually clusters employ a distance measure to calculate the similarity between objects. So, objects within a cluster are more similar to each other than objects in another cluster. This paper uses real-life datasets to compare the accuracy of the most popular algorithm in each approach.
Keywords:clustering; k-means; hierarchical based; density based; partition based; agnes; dbscan; distance measure
Keywords:clustering; k-means; hierarchical based; density based; partition based; agnes; dbscan; distance measure
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