Nearby algorithm, or K-nearest neighbor (kNN, k-NearestNeighbor) classification algorithm is one of classification data mining technology in the easiest way. The so-called K-nearest neighbor, k nearest neighbor is the meaning of that is that each sample can use its closest neighbors k to represent. K-nearest neighbor (k-Nearest Neighbor, KNN) classification algorithm, is a more mature approach in theory, one of the most simple machine learning algorithm. The idea of the method is: if a sample in feature space of the k most similar (i.e. nearest feature space) most of the samples belonging to a particular category, then the sample may also fall into this category. KNN algorithm, the selected neighbors are already correctly classified objects. The method in a given class decisions based solely on the nearest one category or several samples to determine the category to be sub-sample belongs. KNN method, although in principle also depends on the limit theorem, but in the decision-making categories, with only a very small amount of adjacent samples related. Since the KNN method is mainly limited by the surrounding adjacent samples, rather than a method of determining the discrimination class Category field, so for class field of an overlap or more sub-sample set is to be, more than other methods KNN. suitable. (The above information comes from Baidu Encyclopedia)
References
1. Neighbor Algorithm