A framework for feature selection in clustering.

A framework for feature selection in clustering.
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DOI:
10.1198/jasa.2010.tm09415
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发表时间:
2010-06-01
影响因子:
3.7
通讯作者:
Tibshirani R
Tibshirani R
中科院分区:
数学1区
文献类型:
--
作者:
Witten DM;Tibshirani R

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我们考虑的问题,聚类观察使用一个潜在的大的功能集。人们可能会期望数据中存在的真正的潜在聚类仅在一小部分特征方面有所不同,如果使用完整的特征集对观察结果进行聚类,则会错过。我们提出了一个新的框架稀疏聚类,其中一个集群的观察使用自适应选择的功能子集。该方法使用套索型惩罚来选择特征。我们使用这个框架来开发简单的方法稀疏K-means和稀疏层次聚类。一个单一的标准既决定了特征的选择,也决定了结果的聚类。这些方法在模拟数据和基因组数据集上得到了证明。
We consider the problem of clustering observations using a potentially large set of features. One might expect that the true underlying clusters present in the data differ only with respect to a small fraction of the features, and will be missed if one clusters the observations using the full set of features. We propose a novel framework for sparse clustering, in which one clusters the observations using an adaptively chosen subset of the features. The method uses a lasso-type penalty to select the features. We use this framework to develop simple methods for sparse K-means and sparse hierarchical clustering. A single criterion governs both the selection of the features and the resulting clusters. These approaches are demonstrated on simulated data and on genomic data sets.
DOI: 10.1038/nature06258
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