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The silhouette value is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The silhouette ranges from −1 to +1, where a high value indicates that the object is well matched to its own cluster and poorly matched to neighboring clusters.
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]
The Dunn index (DI) (introduced by J. C. Dunn in 1974) is a metric for evaluating clustering algorithms. [1] [2] This is part of a group of validity indices including the Davies–Bouldin index or Silhouette index, in that it is an internal evaluation scheme, where the result is based on the clustered data itself.
Given a data set of n points: {x 1, ..., x n}, and the assignment of these points to k clusters: {C 1, ..., C k}, the Calinski–Harabasz (CH) Index is defined as the ratio of the between-cluster separation (BCSS) to the within-cluster dispersion (WCSS), normalized by their number of degrees of freedom:
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This chart shows the most common display resolutions, with the color of each resolution type indicating the display ratio (e.g., red indicates a 4:3 ratio). This article lists computer monitor, television, digital film, and other graphics display resolutions that are in common use. Most of them use certain preferred numbers.
HDTV ratio used by Silicon Graphics computers in the 1990s, with the resolution being specified as 1920×1024. 1.9:1 SMPTE/DCI digital cinema basic resolution container aspect ratio. Exact ratio is 256:135 but it is commonly referred to as 1.9:1 [32] [33] or 1.90:1 [34] [35] and sometimes 1.896:1. [36] Used by Diao Yinan's The Wild Goose Lake ...