Abstract
Clustering is a common technique for statistical data analysis, Clustering is the process of grouping similar objects into different groups, or more precisely, the partitioning of a data set into subsets according to some defined distance measure. Clustering is an unsupervised learning technique, where interesting patterns and structures can be found directly from very large data sets with little or none of the background knowledge. It is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. In this research, the most representative algorithms K-Means and K-Medoids were examined and analyzed based on their basic approach.
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Madhulatha, T.S. (2011). Comparison between K-Means and K-Medoids Clustering Algorithms. In: Wyld, D.C., Wozniak, M., Chaki, N., Meghanathan, N., Nagamalai, D. (eds) Advances in Computing and Information Technology. ACITY 2011. Communications in Computer and Information Science, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22555-0_48
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DOI: https://doi.org/10.1007/978-3-642-22555-0_48
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