k-Medoids Clustering Algorithm
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A k-Medoids Clustering Algorithm is a partitioning relocation clustering algorithm that choses medoids.
- Context:
- It can be applied by a k-Medoids Clustering System (that solve s a k-Medoids clustering task).
- …
- Example(s):
- a Distance-based k-Medoids, such as:
- ...
- Counter-Example(s):
- See: Weighted Subspace k-Medoids Algorithm.
References
2024
- (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/k-medoids Retrieved:2024-3-22.
- The k-medoids problem is a clustering problem similar to k-means. The name was coined by Leonard Kaufman and Peter J. Rousseeuw with their PAM (Partitioning Around Medoids) algorithm. Both the k-means and k-medoids algorithms are partitional (breaking the dataset up into groups) and attempt to minimize the distance between points labeled to be in a cluster and a point designated as the center of that cluster. In contrast to the k-means algorithm, k-medoids chooses actual data points as centers (medoids or exemplars), and thereby allows for greater interpretability of the cluster centers than in k-means, where the center of a cluster is not necessarily one of the input data points (it is the average between the points in the cluster). Furthermore, k-medoids can be used with arbitrary dissimilarity measures, whereas k-means generally requires Euclidean distance for efficient solutions. Because k-medoids minimizes a sum of pairwise dissimilarities instead of a sum of squared Euclidean distances, it is more robust to noise and outliers than k-means.
k-medoids is a classical partitioning technique of clustering that splits the data set of n objects into k clusters, where the number k of clusters assumed known a priori (which implies that the programmer must specify k before the execution of a k-medoids algorithm). The "goodness" of the given value of k can be assessed with methods such as the silhouette method.
The medoid of a cluster is defined as the object in the cluster whose sum (and, equivalently, the average) of dissimilarities to all the objects in the cluster is minimal, that is, it is a most centrally located point in the cluster.
- The k-medoids problem is a clustering problem similar to k-means. The name was coined by Leonard Kaufman and Peter J. Rousseeuw with their PAM (Partitioning Around Medoids) algorithm. Both the k-means and k-medoids algorithms are partitional (breaking the dataset up into groups) and attempt to minimize the distance between points labeled to be in a cluster and a point designated as the center of that cluster. In contrast to the k-means algorithm, k-medoids chooses actual data points as centers (medoids or exemplars), and thereby allows for greater interpretability of the cluster centers than in k-means, where the center of a cluster is not necessarily one of the input data points (it is the average between the points in the cluster). Furthermore, k-medoids can be used with arbitrary dissimilarity measures, whereas k-means generally requires Euclidean distance for efficient solutions. Because k-medoids minimizes a sum of pairwise dissimilarities instead of a sum of squared Euclidean distances, it is more robust to noise and outliers than k-means.
2012
- http://en.wikipedia.org/wiki/K-medoids
- The k-medoids algorithm is a clustering algorithm related to the k-means algorithm and the medoidshift algorithm. Both the k-means and k-medoids algorithms are partitional (breaking the dataset up into groups) and both attempt to minimize squared error, the distance between points labeled to be in a cluster and a point designated as the center of that cluster. ...
2011
- (Jin & Han, 2011d) ⇒ Xin Jin, and Jiawei Han. (2011). “K-Medoids Clustering.” In: (Sammut & Webb, 2011) p.564
2009
- (Park & Jun, 2009) ⇒ Hae-Sang Park, and Chi-Hyuck Jun. (2009). “A Simple and Fast Algorithm for K-medoids Clustering.” In: Expert Syst. Appl., 36(2). doi:10.1016/j.eswa.2008.01.039
2002
- (Berkhin, 2002) ⇒ Pavel Berkhin. (2002). “A Survey of Clustering Data Mining Techniques." Technical Report, Accrue Software.