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The number of clusters chosen should therefore be 4. In cluster analysis, the elbow method is a heuristic used in determining the number of clusters in a data set. The method consists of plotting the explained variation as a function of the number of clusters and picking the elbow of the curve as the number of clusters to
The inter-cluster distance d(i,j) between two clusters may be any number of distance measures, such as the distance between the centroids of the clusters. Similarly, the intra-cluster distance d '(k) may be measured in a variety of ways, such as the maximal distance between any pair of elements in cluster k. Since internal criterion seek ...
The Jenks classification method is commonly used in thematic maps, especially choropleth maps, as one of several available classification methods. When making choropleth maps, the Jenks classification method can be advantageous because if there are clusters in the data values, it will identify them.
The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]
In this resulting algorithm, the threshold parameter is calculated from the maximum cluster radius and the minimum distance between clusters, which are often known. This method proved to be efficient for data sets of tens of thousands of clusters. If going beyond that amount, a supercluster splitting problem is introduced.
If the data have three clusters, the 2-dimensional plane spanned by three cluster centroids is the best 2-D projection. This plane is also defined by the first two PCA dimensions. Well-separated clusters are effectively modelled by ball-shaped clusters and thus discovered by k-means. Non-ball-shaped clusters are hard to separate when they are ...
Consensus clustering is a method of aggregating (potentially conflicting) results from multiple clustering algorithms.Also called cluster ensembles [1] or aggregation of clustering (or partitions), it refers to the situation in which a number of different (input) clusterings have been obtained for a particular dataset and it is desired to find a single (consensus) clustering which is a better ...
The probability that candidate clusters spawn from the same distribution function (V-linkage). The product of in-degree and out-degree on a k-nearest-neighbour graph (graph degree linkage). [14] The increment of some cluster descriptor (i.e., a quantity defined for measuring the quality of a cluster) after merging two clusters. [15] [16] [17]
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