An Improved Clustering Algorithm
Rui Xin, Chunhong Duo · 2008
The K-means algorithm based on partition and the DBSCAN algorithm based on density are analyzed. Combining advantages with disadvantages of the two algorithms, the improved algorithm DBSK is proposed. Because of the partition of data set, DBSK reduces the requirement of memory; the method of computing variable value is put forward; to the uneven data set, because of adopting different variable values in each local data set, the dependence on global parameters is reduced, so the clustering result is better. Simulative experiment is carried out, which proves the algorithmpsilas feasibility and validity.