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The normalized Google distance (NGD) is a semantic similarity measure derived from the number of hits returned by the Google search engine for a given set of keywords. [1] Keywords with the same or similar meanings in a natural language sense tend to be "close" in units of normalized Google distance, while words with dissimilar meanings tend to ...
We need outside information about what the name means. Using a data base (such as the internet) and a means to search the database (such as a search engine like Google) provides this information. Every search engine on a data base that provides aggregate page counts can be used in the normalized Google distance (NGD). A python package for ...
The normalized compression distance has been used to fully automatically reconstruct language and phylogenetic trees. [ 2 ] [ 3 ] It can also be used for new applications of general clustering and classification of natural data in arbitrary domains, [ 3 ] for clustering of heterogeneous data, [ 3 ] and for anomaly detection across domains. [ 5 ]
NGD (normalized Google distance): (+) large vocab, because it uses any search engine (like Google); (−) can measure relatedness between whole sentences or documents but the larger the sentence or document, the more ingenuity is required (Cilibrasi & Vitanyi, 2007). [46]
In the simplest cases, normalization of ratings means adjusting values measured on different scales to a notionally common scale, often prior to averaging. In more complicated cases, normalization may refer to more sophisticated adjustments where the intention is to bring the entire probability distributions of adjusted values into alignment.
Pages in category "Statistical distance" The following 38 pages are in this category, out of 38 total. ... Normalized Google distance; O. Optimal matching; P.
The normalized angle, referred to as angular distance, between any two vectors and is a formal distance metric and can be calculated from the cosine similarity. [5] The complement of the angular distance metric can then be used to define angular similarity function bounded between 0 and 1, inclusive.
The Mahalanobis distance is a measure of the distance between a point and a distribution, introduced by P. C. Mahalanobis in 1936. [1] The mathematical details of Mahalanobis distance first appeared in the Journal of The Asiatic Society of Bengal in 1936. [ 2 ]