An Improved Clustering Algorithm

Author

Rui, Xin ; Chunhong, Duo

Author_Institution

HeBei Electr. Power Res. Inst., Shijiazhuang

Volume

1

fYear

2008

fDate

17-18 Oct. 2008

Firstpage

394

Lastpage

397

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Design, 2008. ISCID '08. International Symposium on

Conference_Location

Wuhan

Print_ISBN

978-0-7695-3311-7

Type

conf

DOI

10.1109/ISCID.2008.218

Filename

4725634