Clustering by Detecting Density Peaks and Assigning Points by Similarity-First Search Based on Weighted K-Nearest Neighbors Graph
Algorithm 2
Similarity-first search allocation strategy.
Require: , set of cluster centers , number of neighbors , similarity matrix , and SNN average distance matrix
Ensure: point
(1)
Initialize the descending queue and the path queue . The K-nearest neighbors of point are sorted in the ascending order of similarity and pushed into Q. Push M into P.
(2)
while tail point of P do
(3)
if the highest similarity point is unique. then
(4)
Pop a point this at Q’s tail
(5)
else
(6)
Select a point this with the smallest DSNN
(7)
end if
(8)
Empty descending queue Q
(9)
The K-nearest neighbors of this are sorted in the ascending order of similarity and pushed into Q.
