"A modeling technique that assigns values to points based on the values of the k nearby points, such as average value, or most common value." (DAMA International, "The DAMA Dictionary of Data Management", 2011)
"A simple and popular classifier algorithm that assigns a class (in a preexisting classification) to an object whose class is unknown. [...] From a collection of data objects whose class is known, the algorithm computes the distances from the object of unknown class to k (a number chosen by the user) objects of known class. The most common class (i.e., the class that is assigned most often to the nearest k objects) is assigned to the object of unknown class." (Jules H Berman, "Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information", 2013)
"A method used for classification and regression. Cases are analyzed, and class membership is assigned based on similarity to other cases, where cases that are similar (or 'near' in characteristics) are known as neighbors." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)
"A prediction method, which uses a function of the k most similar observations from the training set to generate a prediction, such as the mean." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)
"K-Nearest Neighbors classification is an instance-based supervised learning method that works well with distance-sensitive data." (Matthew Kirk, "Thoughtful Machine Learning", 2015)
"An algorithm that estimates an unknown data item as being like the majority of the k-closest neighbors to that item." (David Natingga, "Data Science Algorithms in a Week" 2nd Ed., 2018)
"K-nearest neighbourhood is a algorithm which stores all available cases and classifies new cases based on a similarity measure. It is used in statistical estimation and pattern recognition." (Aman Tyagi, "Healthcare-Internet of Things and Its Components: Technologies, Benefits, Algorithms, Security, and Challenges", 2021)
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