DocumentCode
2547656
Title
Clustering algorithm on high-dimension data partitional mended attribute
Author
Zhan, Tangsen ; Zhou, Yuanguo
Author_Institution
Sch. of Inf. Eng., Jingdezhen Ceramica Inst., Jingdezhen, China
fYear
2012
fDate
29-31 May 2012
Firstpage
676
Lastpage
678
Abstract
In this paper, a clustering algorithm on high-dimension data partitional mended attribute is put foreword. First of all, through partition of the attribute value and discriminant degree of element, Conjunctive discriminant rules on training set are got. secondly, different discriminant rules on base of mended attribute are found. Experimental results show: the judgement rules got through the training set can better discriminate the test set. thereby, Experimental results verify the effectiveness of the proposed algorithm.
Keywords
pattern clustering; clustering algorithm; conjunctive discriminant rules; discriminant degree of element; discriminant rules; high-dimension data partitional mended attribute; judgement rules; training set; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Data mining; Partitioning algorithms; Statistical analysis; Training; discriminant rules; distinguished degree; mended attribute; partitional interzone;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on
Conference_Location
Sichuan
Print_ISBN
978-1-4673-0025-4
Type
conf
DOI
10.1109/FSKD.2012.6234074
Filename
6234074
Link To Document