A feature group weighting method for subspace clustering of high-dimensional data

	A feature group weighting method for subspace clustering of high-dimensional data
	Xiaojun Chen; Yunming Ye; Xiaofei Xu; Joshua Zhexue Huang
刊名	PATTERN RECOGNITION
	2012
英文摘要	This paper proposes a new method to weight subspaces in feature groups and individual features for clustering high-dimensional data. In this method, the features of high-dimensional data are divided into feature groups, based on their natural characteristics. Two types of weights are introduced to the clustering process to simultaneously identify the importance of feature groups and individual features in each cluster. A new optimization model is given to define the optimization process and a new clustering algorithm FG-k-means is proposed to optimize the optimization model. The new algorithm is an extension to k-means by adding two additional steps to automatically calculate the two types of subspace weights. A new data generation method is presented to generate high-dimensional data with clusters in subspaces of both feature groups and individual features. Experimental results on synthetic and real-life data have shown that the FG-k-means algorithm significantly outperformed four k-means type algorithms, i.e., k-means, W-k-means, LAC and EWKM in almost all experiments. The new algorithm is robust to noise and missing values which commonly exist in high-dimensional data.
收录类别	SCI
原文出处	http://www.sciencedirect.com/science/article/pii/S003132031100269X
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/4149]
专题	深圳先进技术研究院_数字所
作者单位	PATTERN RECOGNITION
推荐引用方式 GB/T 7714	Xiaojun Chen,Yunming Ye,Xiaofei Xu,et al. A feature group weighting method for subspace clustering of high-dimensional data[J]. PATTERN RECOGNITION,2012.
APA	Xiaojun Chen,Yunming Ye,Xiaofei Xu,&Joshua Zhexue Huang.(2012).A feature group weighting method for subspace clustering of high-dimensional data.PATTERN RECOGNITION.
MLA	Xiaojun Chen,et al."A feature group weighting method for subspace clustering of high-dimensional data".PATTERN RECOGNITION (2012).