Functional clustering by Bayesian wavelet methods
Functional clustering by Bayesian wavelet methods
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DOI：
10.1111/j.1467-9868.2006.00545.x
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发表时间：
2006-01-01
影响因子：
5.8
通讯作者：
Mallick, B
中科院分区：
文献类型：
--
作者：
Ray, S;Mallick, B
提出了一种非参数贝叶斯小波模型用于函数型数据的聚类。基于小波的方法是针对通用的全局和局部特征的决议，在聚类过程中，是适合于聚类高维数据。基于Dirichlet过程，非参数贝叶斯模型将传统贝叶斯小波方法的适用范围扩展到函数聚类，并通过适当混合Dirichlet过程，使函数的正则性和聚类数的先验信息得以获取.后验推理是通过共轭先验的Gibbs抽样进行的，这使得计算简单。我们使用模拟以及真实的数据集来说明其他替代品的方法的适用性。
We propose a nonparametric Bayes wavelet model for clustering of functional data. The wavelet-based methodology is aimed at the resolution of generic global and local features during clustering and is suitable for clustering high dimensional data. Based on the Dirichlet process, the nonparametric Bayes model extends the scope of traditional Bayes wavelet methods to functional clustering and allows the elicitation of prior belief about the regularity of the functions and the number of clusters by suitably mixing the Dirichlet processes. Posterior inference is carried out by Gibbs sampling with conjugate priors, which makes the computation straightforward. We use simulated as well as real data sets to illustrate the suitability of the approach over other alternatives.