DocumentCode
2113237
Title
Clustering Ensemble Based on Hierarchical Partition
Author
Li, Taoying ; Chen, Yan
Author_Institution
Transp. Manage. Collage, Dalian Maritime Univ., Dalian, China
fYear
2009
fDate
20-22 Sept. 2009
Firstpage
1
Lastpage
4
Abstract
Many clustering ensemble algorithms need to predesign initial thresholds before partition data points, which is supervised learning and directly influence the efficiency of clustering. In order to cluster data points under fully unsupervised situation, the hierarchical partition is introduced in this paper. The proposed algorithm makes use of the distribution of results of all clustering memberships by constructing the m-subset of Descartes with the support degree. The theorems and definitions advanced in this paper are detailed proved. Finally, the proposed algorithm is applied in practice and results show that it is effective.
Keywords
hierarchical systems; learning (artificial intelligence); pattern clustering; set theory; Descartes m-subset; clustering ensemble algorithm; hierarchical partition; partition data point; predesign initial threshold; supervised learning; Algorithm design and analysis; Assembly; Clustering algorithms; Data mining; Information processing; Nearest neighbor searches; Partitioning algorithms; Supervised learning; Text recognition; Transportation;
fLanguage
English
Publisher
ieee
Conference_Titel
Management and Service Science, 2009. MASS '09. International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-4638-4
Electronic_ISBN
978-1-4244-4639-1
Type
conf
DOI
10.1109/ICMSS.2009.5302536
Filename
5302536
Link To Document