Clustering with Decision Trees: Divisive and Agglomerative Approach
Clustering with Decision Trees: Divisive and Agglomerative Approach
复制标题
决策树聚类：分裂和凝聚方法
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Benoît Frénay
中科院分区：
文献类型：
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作者：
Lauriane Castin;Benoît Frénay
.决策树主要用于执行分类任务。样本在树的每个节点中被提交到测试，并基于结果被引导通过树。决策树也可以用于执行聚类，只需进行一些调整。一方面，必须发现新的分裂准则来构造树，而不需要样本标签的知识。另一方面，必须应用新的算法将叶节点处的子簇合并为实际簇。在本文中，新的分裂准则和凝聚算法的聚类，与其他现有的聚类技术的结果。
. Decision trees are mainly used to perform classification tasks. Samples are submitted to a test in each node of the tree and guided through the tree based on the result. Decision trees can also be used to perform clustering, with a few adjustments. On one hand, new split criteria must be discovered to construct the tree without the knowledge of samples labels. On the other hand, new algorithms must be applied to merge sub-clusters at leaf nodes into actual clusters. In this paper, new split criteria and agglomeration algorithms are developed for clustering, with results comparable to other existing clustering techniques.