摘要翻译：
在面向属性归纳的广义关系阈值控制策略的第六步中，发现有趣的规则，可以选择候选属性进行进一步的泛化和合并，直到相同的元组数目不超过阈值，就像基本的面向属性归纳算法中实现的那样。在此策略步骤中，最终泛化结果中元组的个数仍有可能大于阈值。为了最终得到元组数目少且易于转化为简单逻辑公式的泛化结果，提出了规则转换的第七步策略，将相同的属性统一或分组进行简化。与Fudger和Hamilton的启发式度量方法相反，概念层次越复杂，就越有可能找到有趣的结果，而概念层次越简单，就越有可能找到有趣的结果，概念层次越复杂，就越有可能在概念树中得到复杂的过程推广。发现有趣规则的决定受到概念树的宽度、长度、深度或层次的影响。
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英文标题：
《Measuring interesting rules in Characteristic rule》
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作者：
Spits Warnars
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Databases        数据库
分类描述：Covers database management, datamining, and data processing. Roughly includes material in ACM Subject Classes E.2, E.5, H.0, H.2, and J.1.
涵盖数据库管理、数据挖掘和数据处理。大致包括ACM学科类E.2、E.5、H.0、H.2和J.1中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Finding interesting rule in the sixth strategy step about threshold control on generalized relations in attribute oriented induction, there is possibility to select candidate attribute for further generalization and merging of identical tuples until the number of tuples is no greater than the threshold value, as implemented in basic attribute oriented induction algorithm. At this strategy step there is possibility the number of tuples in final generalization result still greater than threshold value. In order to get the final generalization result which only small number of tuples and can be easy to transfer into simple logical formula, the seventh strategy step about rule transformation is evolved where there will be simplification by unioning or grouping the identical attribute. Our approach to measure interesting rule is opposite with heuristic measurement approach by Fudger and Hamilton where the more complex concept hierarchies, more interesting results are likely to be found, but our approach the simpler concept hierarchies, more interesting results are likely to be found and the more complex concept hierarchies, more complex process generalization in concept tree. The decision to find interesting rule is influenced with wide or length and depth or level of concept tree.
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PDF链接：
https://arxiv.org/pdf/1006.1692