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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Dunn index ： ウィキペディア英語版 Dunn index The Dunn index (DI) (introduced by J. C. Dunn in 1974) is a metric for evaluating clustering algorithms. This is part of a group of validity indices including the Davies–Bouldin index, in that it is an internal evaluation scheme, where the result is based on the clustered data itself. As do all other such indices, the aim is to identify sets of clusters that are compact, with a small variance between members of the cluster, and well separated, where the means of different clusters are sufficiently far apart, as compared to the within cluster variance. For a given assignment of clusters, a higher Dunn index indicates better clustering. One of the drawbacks of using this, is the computational cost as the number of clusters and dimensionality of the data increase. ==Preliminaries== There are many ways to define the size or diameter of a cluster. It could be the distance between the farthest two points inside a cluster, it could be the mean of all the pairwise distances between data points inside the cluster, or it could as well be the distance of each data point from the cluster centroid. Each of these formulations are mathematically shown below: Let ''C''''i'' be a cluster of vectors. Let ''x'' and ''y'' be any two n dimensional feature vectors assigned to the same cluster ''C''''i''. : $\backslash Delta\_i\; =\; \backslash underset\; \backslash underset\; d(x,y)$ , which calculates the mean distance between all pairs. : $\backslash Delta\_i\; =\; \backslash dfrac\; d(x,\backslash mu)\}\; ,\; \backslash mu\; =\; \backslash dfrac\; x\}$ , calculates distance of all the points from the mean. This can also be said about the intercluster distance, where similar formulations can be made, using either the closest two data points, one in each cluster, or the farthest two, or the distance between the centroids and so on. The definition of the index includes any such formulation, and the family of indices so formed are called Dunn-like Indices. Let : $\backslash delta(C\_i,C\_j)$ be this intercluster distance metric, between clusters ''C''''i'' and ''C''''j''. 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「Dunn index」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.