Evolution of Multiple Tree Structured Patterns from Tree-Structured Data Using Clustering
Evolution of Multiple Tree Structured Patterns from Tree-Structured Data Using Clustering
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DOI：
10.1007/978-3-540-89378-3_51
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发表时间：
2008-12
期刊：
影响因子：
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通讯作者：
Masatoshi Nagamine;T. Miyahara;T. Kuboyama;H. Ueda;Kenichi Takahashi
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作者：
Masatoshi Nagamine;T. Miyahara;T. Kuboyama;H. Ueda;Kenichi Takahashi
我们提出了一种新的遗传规划方法，利用聚类技术从树状结构数据中提取多个树状结构模式。作为组合模式，我们使用一组树结构模式，称为标记树模式。标记树模式中的结构化变量可以用任意树代替。如果一组标记树模式中至少有一个与树匹配，则一组标记树模式与树匹配。通过对正数据进行聚类，并在每个负数据聚类上运行GP子进程，得到由GP子进程中最优个体组成的组合模式。在一些聚糖数据上的实验表明，我们提出的方法具有较高的支持度，约为0.8，而先前的单模式进化方法的支持度较低，约为0.5。
We propose a new genetic programming approach to extraction of multiple tree structured patterns from tree-structured data using clustering. As a combined pattern we use a set of tree structured patterns, called tag tree patterns. A structured variable in a tag tree pattern can be substituted by an arbitrary tree. A set of tag tree patterns matches a tree, if at least one of the set of patterns matches the tree. By clustering positive data and running GP subprocesses on each cluster with negative data, we make a combined pattern which consists of best individuals in GP subprocesses. The experiments on some glycan data show that our proposed method has a higher support of about 0.8 while the previous method for evolving single patterns has a lower support of about 0.5.