KATE is using a version of the nearest neighbor algorithm for computing similarity metrics. A simplified version of this algorithm is described here. For a further description, see [6]. For a discussion on k -Nearest Neighbor algorithms, see [24].
The similarity between two cases x and y having p features is:
Where f is defined as:
The algorithm is then:
Classified Data = 0 for each Case x in Casebase do 1. for each y in Classified Data do Sim(y) = Similarity(y,x) 2. y_max = (y_1,...,y_k) such that Sim(y_k) = max(K-nearest neighbors) 3. if class(y_max) = class(x) then classification is correct Classified Data = Classified Data + {x} else classification is incorrect