Clustering criteria

measure of similarity and diversity between sets

thumb|350px|Precision and recall In statistical analysis of binary classification and information retrieval systems, the F-score or F-measure is a measure of predictive performance. It is calculated from the precision and recall of the test, where the precision is the number of true positive results divided by the number of all samples predicted to be positive, including those not identified correctly, and the recall is the number of true positive results divided by the number of all samples that should have been identified as positive. Precision is also known as positive predictive value, and

method in cluster analysis

function that quantifies the similarity between two objects

metric for evaluating clustering algorithms

measure of similarity between two data clusterings

metric for evaluating clustering algorithms

In computer science and data mining, MinHash (or the min-wise independent permutations locality sensitive hashing scheme) is a technique for quickly estimating how similar two sets are. The scheme was published by Andrei Broder in a 1997 conference, and initially used in the AltaVista search engine to detect duplicate web pages and eliminate them from search results. It has also been applied in large-scale clustering problems, such as clustering documents by the similarity of their sets of words.