Three Improved Algorithms for Optimizing of Randomly-initiated K-means Midpoints
Wang Yan · Huagong zidonghua ji yibiao · 2012
Aiming at traditional K-means which randomly generating initial clustering centers,the N2-K-means,F2-K-means and SA-K-means center point optimization algorithms were proposed,in which,the N2-K-means determines group K by looking for Euclideannearby points in multidimensional space;and F2-K-means employs a very far neighbor method;and the SA-K-means converts the data sets from the separate data into matrix,and then has them normalized and dualized to calculate the dissimilarity between every vector so as to modify Euclidean distance of each initial center.This three algorithms suitable for different spaces can improve the traditional K-means algorithm and promote its stability and accuracy.