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K-means clustering adopting rbf-kernel

หน่วยงาน Central Queensland University, Australia


ชื่อเรื่อง : K-means clustering adopting rbf-kernel
นักวิจัย : Ali, Shawkat.
คำค้น : Data mining. , Not a CQU Research Flagship , Pattern perception. , Expert systems (Computer science) , Artificial intelligence. , 700199 Computer software and services not elsewhere classified , 280201 Expert Systems , 280207 Pattern Recognition , 280213 Other Artificial Intelligence , Clustering -- K-means -- rbf-Kernel -- Automated parameter selection
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2550
อ้างอิง : http://hdl.cqu.edu.au/10018/16855 , cqu:3128
ที่มา : Shawkat Ali, ABM 2007, 'K-means Clustering Adopting rbf-Kernelc', in D Taniar (ed.), Data Mining and Knowledge Discovery Technologies, IGI Global, Hershey, PA, USA.
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Data mining and knowledge discovery technologies / David Taniar, [editor] Hershey, PA, USA : IGI Global, 2007. Chapter VI, p. 118-142 369 pages XIV chapters 9781599049601 9781599049618 (online) , aCQUIRe [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

Clustering technique in data mining has received a significant amount of attention from machine learning community in the last few years as one of the fundamental research areas. Among the vast range of clustering algorithms, K-means is one of the most popular clustering algorithm. In this research we extend K-means algorithm by adding well known radial basis function (rbf) kernel and find better performance than classical K-means algorithm. It is a critical issue for rbf kernel, how can we select a unique parameter for optimum clustering task. This present chapter will provide a statistical based solution on this issue. The best parameter selection is considered on the basis of prior information of the data by Maximum Likelihood (ML) method and Nelder-Mead (N-M) simplex method. A rule based meta-learning approach is then proposed for automatic rbf kernel parameter selection.We consider 112 upervised data set and measure the statistical data characteristics using basic statistics, central tendency measure and entropy based approach. We split this data characteristics using well known decision tree approach to generate the rules. Finally we use the generated rules to select the unique parameter value for rbf kernel and then adopt in K-means algorithm. The experiment has been demonstrated with 112 problems and 10 fold cross validation methods. Finally the proposed algorithm can solve any clustering task very quickly with optimum performance.

บรรณานุกรม :
Ali, Shawkat. . (2550). K-means clustering adopting rbf-kernel.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Ali, Shawkat. . 2550. "K-means clustering adopting rbf-kernel".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Ali, Shawkat. . "K-means clustering adopting rbf-kernel."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2550. Print.
Ali, Shawkat. . K-means clustering adopting rbf-kernel. กรุงเทพมหานคร : Central Queensland University, Australia; 2550.