Details

Autor(en) / Beteiligte

• Huang, J.Z.
• Ng, M.K.
• Hongqiang Rong
• Zichen Li

Titel

Automated variable weighting in k-means type clustering

Ist Teil von

• IEEE transactions on pattern analysis and machine intelligence, 2005-05, Vol.27 (5), p.657-668

Ort / Verlag

Los Alamitos, CA: IEEE

Erscheinungsjahr

2005

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• This paper proposes a k-means type clustering algorithm that can automatically calculate variable weights. A new step is introduced to the k-means clustering process to iteratively update variable weights based on the current partition of data and a formula for weight calculation is proposed. The convergency theorem of the new clustering process is given. The variable weights produced by the algorithm measure the importance of variables in clustering and can be used in variable selection in data mining applications where large and complex real data are often involved. Experimental results on both synthetic and real data have shown that the new algorithm outperformed the standard k-means type algorithms in recovering clusters in data.