Creating Abstract Concepts for Classification by Finding Top- N Maximal Weighted Cliques
Creating Abstract Concepts for Classification by Finding Top- N Maximal Weighted Cliques
复制标题
通过查找前 N 个最大加权派系来创建分类抽象概念
DOI：
10.1007/978-3-540-39644-4_41
复制
发表时间：
2003
影响因子：
1.1
通讯作者：
M. Haraguchi
中科院分区：
文献类型：
--
作者：
Yoshiaki Okubo;M. Haraguchi
提出了一种用于分类规则挖掘的抽象概念生成方法。我们试图找到抽象的概念，是有用的分类在这个意义上，假设这样的概念可以很好地区分目标类，并支持尽可能多的数据。我们的任务，发现有用的概念被形式化为一个优化问题，其约束和目标函数分别由熵和类分布的概率。要找到的概念可以用从可能的分布构造的图中的最大加权集团来表示。从图中，作为有用的抽象概念，前N个最大加权团有效地提取两个修剪技术：分支定界和基于熵的修剪。结果表明，我们的基于熵的修剪可以安全地修剪只有无用的集团，通过添加分布在其熵的增加的顺序在集团扩张的过程中。初步的实验结果表明，有用的概念，可以创建在我们的框架。
This paper presents a method for creating abstract concepts for classification rule mining. We try to find abstract concepts that are useful for the classification in the sense that assuming such a concept can well discriminate a target class and supports data as much as possible. Our task of finding useful concepts is formalized as an optimization problem in which its constraint and objective function are given by entropy and probability of class distributions, respectively. Concepts to be found can be stated in terms of maximal weighted cliques in a graph constructed from the possible distributions. From the graph, as useful abstract concepts, top-N maximal weighted cliques are efficiently extracted with two pruning techniques: branch-and-bound and entropy-based pruning. It is shown that our entropy-based pruning can safely prune only useless cliques by adding distributions in increasing order of their entropy in the process of clique expansion. Preliminary experimental results show that useful concepts can be created in our framework.