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  2. Silhouette (clustering) - Wikipedia

    en.wikipedia.org/wiki/Silhouette_(clustering)

    If many points have a low or negative value, then the clustering configuration may have too many or too few clusters. A clustering with an average silhouette width of over 0.7 is considered to be "strong", a value over 0.5 "reasonable" and over 0.25 "weak", but with increasing dimensionality of the data, it becomes difficult to achieve such ...

  3. Determining the number of clusters in a data set - Wikipedia

    en.wikipedia.org/wiki/Determining_the_number_of...

    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]

  4. Automatic clustering algorithms - Wikipedia

    en.wikipedia.org/wiki/Automatic_Clustering...

    Automatic clustering algorithms are algorithms that can perform clustering without prior knowledge of data sets. In contrast with other cluster analysis techniques, automatic clustering algorithms can determine the optimal number of clusters even in the presence of noise and outlier points. [1] [needs context]

  5. Dunn index - Wikipedia

    en.wikipedia.org/wiki/Dunn_index

    The Dunn index, introduced by Joseph 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.

  6. Most vexing parse - Wikipedia

    en.wikipedia.org/wiki/Most_vexing_parse

    The most vexing parse is a counterintuitive form of syntactic ambiguity resolution in the C++ programming language. In certain situations, the C++ grammar cannot distinguish between the creation of an object parameter and specification of a function's type. In those situations, the compiler is required to interpret the line as a function type ...

  7. Clustering high-dimensional data - Wikipedia

    en.wikipedia.org/wiki/Clustering_high...

    Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions.Such high-dimensional spaces of data are often encountered in areas such as medicine, where DNA microarray technology can produce many measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions ...

  8. C string handling - Wikipedia

    en.wikipedia.org/wiki/C_string_handling

    This was recognized as a defect in the standard and fixed in C++.) [4] C++11 and C11 add two types with explicit widths char16_t and char32_t. [5] Variable-width encodings can be used in both byte strings and wide strings. String length and offsets are measured in bytes or wchar_t, not in "characters", which can be confusing to beginning ...

  9. Category:Clustering criteria - Wikipedia

    en.wikipedia.org/wiki/Category:Clustering_criteria

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