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	The key data mining models for high dimensional data
	Deng, Xiang ; Wang, Beizhan ; Wei, Haifang ; Chen, Minkui ; Wang BZ(王备战)
	2013
关键词	Bioinformatics Classification (of information) Clustering algorithms Communication
英文摘要	Conference Name:International Conference on Communication, Electronics, and Automation Engineering, 2012. Conference Address: Xi'an, China.; Xi'an Technological University; Shaanxi New Network and Monitoring Control Engineering Laboratory; With the rapid development of computational biology and e-commerce applications, high-dimensional data becomes more and more powerful. Thus, it is an urgent problem of great importance when mining high-dimensional data. However, there are some challenges for mining data of high dimensions, the first one is the curse of dimensionality and the second one is the meaningfulness of the similarity measure in the high dimension space. In this paper, we present several state-of-art techniques for constructing three data mining models with analyzing high-dimensional data, these models include frequent pattern mining, clustering, and classification. And we discuss how these methods deal with the challenges of high dimensionality. ? 2013 Springer-Verlag.
语种	英语
出处	http://dx.doi.org/10.1007/978-3-642-31698-2_46
出版者	Springer Verlag
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/85745]
专题	软件学院－会议论文
推荐引用方式 GB/T 7714	Deng, Xiang,Wang, Beizhan,Wei, Haifang,et al. The key data mining models for high dimensional data. 2013-01-01.

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