In a single sentence, nearest neighbor classifiers are defined by their characteristic of classifying unlabeled examples by assigning them the class of similar labeled examples. Despite the simplicity of this idea, nearest neighbor methods are extremely powerful. They have been used successfully for:
Computer vision applications, including optical character recognition and facial recognition in both still images and video
Predicting whether a person will enjoy a movie or music recommendation
Identifying patterns in genetic data, perhaps to use them in detecting specific proteins or diseases
In general, nearest neighbor classifiers are well-suited for classification tasks, where relationships among the features and the target classes are numerous, complicated, or extremely difficult to understand, yet the items of similar class type tend to be fairly homogeneous. Another way of putting it would be to say that if a concept is difficult to define, but...