Title :
An Improved Clustering Algorithm
Author :
Rui, Xin ; Chunhong, Duo
Author_Institution :
HeBei Electr. Power Res. Inst., Shijiazhuang
Abstract :
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.
Keywords :
computational complexity; data mining; pattern clustering; sampling methods; DBSCAN algorithm; DBSK algorithm; K-means clustering algorithm; data mining; data set partitioning; memory requirement reduction; sampling complexity; Algorithm design and analysis; Clustering algorithms; Computational intelligence; Costs; Data mining; Databases; Iterative algorithms; Partitioning algorithms; Pattern recognition; Sampling methods; DBSCAN; K-means; clustering technology; data mining;
Conference_Titel :
Computational Intelligence and Design, 2008. ISCID '08. International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3311-7
DOI :
10.1109/ISCID.2008.218
