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 :
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