Description: K-Medoids is a clustering method used in unsupervised learning, similar to the K-Means algorithm, but with a key difference: instead of using centroids that may not correspond to actual data points, K-Medoids selects actual data points as its cluster centers. This makes it more robust against outliers, as medoids are less sensitive to distortions that can be introduced by extreme data. The algorithm works by assigning each data point to the nearest medoid and then updating the medoids by selecting the point that minimizes the sum of distances to all points in the cluster. This process is repeated until there are no changes in the assignment of points to clusters. K-Medoids is particularly useful in situations where data contains noise or outliers, and it is applied in various fields such as customer segmentation, image analysis, and bioinformatics. Its ability to work with different distance metrics also makes it versatile, allowing its use in a wide range of applications.
Uses: K-Medoids is used in various applications, especially in data analysis where robustness against outliers is crucial. It is employed in customer segmentation to identify groups of consumers with similar behaviors, allowing organizations to tailor their strategies. It is also used in image analysis to group similar pixels and in bioinformatics to classify genes or proteins into functional groups. Its ability to handle different distance metrics makes it suitable for applications where data is not necessarily numerical, such as in text processing or categorical data.
Examples: A practical example of K-Medoids is its use in customer segmentation in a retail business, where customers are grouped based on their purchasing patterns. Another example is in medical image analysis, where different types of tissues or anomalies can be grouped in MRI images. In bioinformatics, K-Medoids has been used to classify DNA sequences into groups that share similar genetic characteristics.
