DocumentCode
524431
Title
The improvement of initial point selection method for fuzzy K-Prototype clustering algorithm
Author
Caiying, Zhou ; Longjun, Huang
Author_Institution
Sci.&Technol. Div., JiangXi Univ. of Sci. & Technol., Ganzhou, China
Volume
4
fYear
2010
fDate
22-24 June 2010
Abstract
K-Prototype is one of the important and effective clustering analysis algorithm to deal with mixed data types. This article discussed fuzzy clustering algorithm based on K-Prototype in detail and made improvements to solve its initial value problems. The proposed method is simple, easy to understand and can be achieved easily.
Keywords
data mining; fuzzy set theory; pattern clustering; effective clustering analysis algorithm; fuzzy k-prototype clustering algorithm; initial point selection method; Algorithm design and analysis; Clustering algorithms; Computer science education; Data analysis; Educational technology; Euclidean distance; Fuzzy sets; Partitioning algorithms; Prototypes; Software algorithms; K-Prototype; clustering analysis; initial value; mixed data types;
fLanguage
English
Publisher
ieee
Conference_Titel
Education Technology and Computer (ICETC), 2010 2nd International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-4244-6367-1
Type
conf
DOI
10.1109/ICETC.2010.5529620
Filename
5529620
Link To Document