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In computer science, the count-distinct problem [1] (also known in applied mathematics as the cardinality estimation problem) is the problem of finding the number of distinct elements in a data stream with repeated elements. This is a well-known problem with numerous applications.
Within each group use the mean for aggregating together the results, and finally take the median of the group estimates as the final estimate. [ 5 ] The 2007 HyperLogLog algorithm splits the multiset into subsets and estimates their cardinalities, then it uses the harmonic mean to combine them into an estimate for the original cardinality.
Alternatively, a user may wish to rearrange the data in order to see the rows ranked by some numerical value, or even based on a combination of values from the original dataset. Functions within the dplyr package will allow a user to perform such tasks. dplyr was launched in 2014. [4] On the dplyr web page, the package is described as "a ...
Using the example above, the software will find all distinct values for Region. In this case, they are: North, South, East, West. Furthermore, it will find all distinct values for Ship date. Based on the aggregation type, sum, it will summarize the fact, the quantities of Unit, and display them in a multidimensional chart. In the example above ...
The statistical treatment of count data is distinct from that of binary data, in which the observations can take only two values, usually represented by 0 and 1, and from ordinal data, which may also consist of integers but where the individual values fall on an arbitrary scale and only the relative ranking is important. [example needed]
Model-based clustering [1] based on a statistical model for the data, usually a mixture model. This has several advantages, including a principled statistical basis for clustering, and ways to choose the number of clusters, to choose the best clustering model, to assess the uncertainty of the clustering, and to identify outliers that do not ...
Count sketch is a type of dimensionality reduction that is particularly efficient in statistics, machine learning and algorithms. [1] [2] It was invented by Moses Charikar, Kevin Chen and Martin Farach-Colton [3] in an effort to speed up the AMS Sketch by Alon, Matias and Szegedy for approximating the frequency moments of streams [4] (these calculations require counting of the number of ...
In mathematics, an injective function (also known as injection, or one-to-one function [1]) is a function f that maps distinct elements of its domain to distinct elements of its codomain; that is, x 1 ≠ x 2 implies f(x 1) ≠ f(x 2) (equivalently by contraposition, f(x 1) = f(x 2) implies x 1 = x 2).