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Example image with only red and green channel (for illustration purposes) Vector quantization of colors present in the image above into Voronoi cells using k-means. Example: In the field of computer graphics, k-means clustering is often employed for color quantization in image compression. By reducing the number of colors used to represent an ...
Both oversampling and undersampling involve introducing a bias to select more samples from one class than from another, to compensate for an imbalance that is either already present in the data, or likely to develop if a purely random sample were taken. Data Imbalance can be of the following types:
It is a random sample because each student in the classroom had an equal chance (1 in 6) of being in the row selected. However, it is NOT a "simple random sample" because not all possible samples of size 10 in this classroom have the same chance of being selected. Thus, any stratified or cluster sampling may begin with a random sample but can ...
Consensus clustering is a method of aggregating (potentially conflicting) results from multiple clustering algorithms.Also called cluster ensembles [1] or aggregation of clustering (or partitions), it refers to the situation in which a number of different (input) clusterings have been obtained for a particular dataset and it is desired to find a single (consensus) clustering which is a better ...
In the theory of finite population sampling, Bernoulli sampling is a sampling process where each element of the population is subjected to an independent Bernoulli trial which determines whether the element becomes part of the sample. An essential property of Bernoulli sampling is that all elements of the population have equal probability of ...
For example, the sample space of a coin flip could be Ω = {"heads", "tails" }. To define probability distributions for the specific case of random variables (so the sample space can be seen as a numeric set), it is common to distinguish between discrete and absolutely continuous random variables.
An estimator δ(X) is an observable random variable (i.e. a statistic) used for estimating some unobservable quantity. For example, one may be unable to observe the average height of all male students at the University of X, but one may observe the heights of a random sample of 40 of them. The average height of those 40—the "sample average ...
A typical formulation of the Hopkins statistic follows. [2]Let be the set of data points. Generate a random sample of data points sampled without replacement from . Generate a set of uniformly randomly distributed data points.