Clustering objects on subsets of attributes
Clustering objects on subsets of attributes
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DOI：
10.1111/j.1467-9868.2004.02059.x
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发表时间：
2004-01-01
影响因子：
5.8
通讯作者：
Meulman, JJ
中科院分区：
文献类型：
--
作者：
Friedman, JH;Meulman, JJ
提出了一种新的属性值数据聚类方法。当与传统的基于距离的聚类算法结合使用时，该过程鼓励这些算法自动检测优先聚类在属性变量的子集上而不是同时聚类在所有属性变量上的对象的子组。每个单独聚类的相关属性子集可以是不同的，并且与其他聚类的相关属性子集部分（或完全）重叠。提高灵敏度，特别是低基数组聚类的一个小子集的变量进行了讨论。在不同领域的应用，包括基因表达阵列。
A new procedure is proposed for clustering attribute value data. When used in conjunction with conventional distance-based clustering algorithms this procedure encourages those algorithms to detect automatically subgroups of objects that preferentially cluster on subsets of the attribute variables rather than on all of them simultaneously. The relevant attribute subsets for each individual cluster can be different and partially (or completely) overlap with those of other clusters. Enhancements for increasing sensitivity for detecting especially low cardinality groups clustering on a small subset of variables are discussed. Applications in different domains, including gene expression arrays, are presented.