Matches in DBpedia 2014 for { <http://dbpedia.org/resource/K-medoids> ?p ?o. }
Showing items 1 to 36 of
36
with 100 items per page.
- K-medoids abstract "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 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 datapoints as centers (medoids or exemplars) and works with an arbitrary matrix of distances between datapoints instead of . This method was proposed in 1987 for the work with norm and other distances.k-medoid is a classical partitioning technique of clustering that clusters the data set of n objects into k clusters known a priori. A useful tool for determining k is the silhouette.It is more robust to noise and outliers as compared to k-means 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.The most common realisation of k-medoid clustering is the Partitioning Around Medoids (PAM) algorithm and is as follows: Initialize: randomly select k of the n data points as the medoids Associate each data point to the closest medoid. ("closest" here is defined using any valid distance metric, most commonly Euclidean distance, Manhattan distance or Minkowski distance) For each medoid m For each non-medoid data point o Swap m and o and compute the total cost of the configuration Select the configuration with the lowest cost. Repeat steps 2 to 4 until there is no change in the medoid.↑ ↑".
- K-medoids thumbnail Kmedoid1.jpg?width=300.
- K-medoids wikiPageExternalLink Kmeans_Kmedoids.html.
- K-medoids wikiPageExternalLink Gap.
- K-medoids wikiPageID "6406095".
- K-medoids wikiPageRevisionID "603693771".
- K-medoids hasPhotoCollection K-medoids.
- K-medoids subject Category:Data_clustering_algorithms.
- K-medoids subject Category:Statistical_algorithms.
- K-medoids type Abstraction100002137.
- K-medoids type Act100030358.
- K-medoids type Activity100407535.
- K-medoids type Algorithm105847438.
- K-medoids type Cognition100023271.
- K-medoids type DataClusteringAlgorithms.
- K-medoids type Datum105816622.
- K-medoids type Event100029378.
- K-medoids type Information105816287.
- K-medoids type Procedure101023820.
- K-medoids type PsychologicalFeature100023100.
- K-medoids type Rule105846932.
- K-medoids type StatisticalAlgorithms.
- K-medoids type YagoPermanentlyLocatedEntity.
- K-medoids comment "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 the distance between points labeled to be in a cluster and a point designated as the center of that cluster.".
- K-medoids label "K-medoids".
- K-medoids label "K-medoids".
- K-medoids label "K-médoïdes".
- K-medoids sameAs K-médoïdes.
- K-medoids sameAs K-medoids.
- K-medoids sameAs m.0g44mf.
- K-medoids sameAs Q3191282.
- K-medoids sameAs Q3191282.
- K-medoids sameAs K-medoids.
- K-medoids wasDerivedFrom K-medoids?oldid=603693771.
- K-medoids depiction Kmedoid1.jpg.
- K-medoids isPrimaryTopicOf K-medoids.
