|
The -medoids algorithm is a clustering algorithm related to the algorithm and the medoidshift algorithm. Both the -means and -medoids algorithms are partitional (breaking the dataset up into groups) and both 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 -means algorithm, -medoids chooses datapoints as centers (medoids or exemplars) and works with an arbitrary matrix of distances between datapoints instead of . This method was proposed in 1987〔Kaufman, L. and Rousseeuw, P.J. (1987), Clustering by means of Medoids, in Statistical Data Analysis Based on the –Norm and Related Methods, edited by Y. Dodge, North-Holland, 405–416.〕 for the work with norm and other distances. -medoid is a classical partitioning technique of clustering that clusters the data set of objects into clusters known ''a priori''. A useful tool for determining is the silhouette. It is more robust to noise and outliers as compared to because it minimizes a sum of pairwise dissimilarities instead of a sum of squared Euclidean distances. A medoid can be defined as the object of a cluster whose average dissimilarity to all the objects in the cluster is minimal. i.e. it is a most centrally located point in the cluster. ==Algorithms== The most common realisation of -medoid clustering is the Partitioning Around Medoids (PAM) algorithm and is as follows: # Initialize: randomly select (without replacement) of the data points as the medoids # Associate each data point to the closest medoid. # While the cost of the configuration decreases: ## For each medoid , for each non-medoid data point : ### Swap and , recompute the cost (sum of distances of points to their medoid) ### If the total cost of the configuration increased in the previous step, undo the swap Other algorithms than PAM have been suggested in the literature, including the following Voronoi iteration method: # Select initial medoids # Iterate while the cost decreases: ## In each cluster, make the point that minimizes the sum of distances within the cluster the medoid ## Reassign each point to the cluster defined by 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「K-medoids」の詳細全文を読む スポンサード リンク
|