Evolutionary Method for Two-dimensional Associative Local Distribution Rule Mining
Evolutionary Method for Two-dimensional Associative Local Distribution Rule Mining
复制标题
二维关联局部分布规则挖掘的进化方法
DOI：
10.1109/ictai52525.2021.00163
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Matsuno Shogo
中科院分区：
文献类型：
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作者：
Shimada Kaoru;Arahira Takaaki;Matsuno Shogo
在本文中，我们提出了一种规则发现方法，可以揭示一个属性的组合，提供两个连续的变量的特征分布直接在高速数据库中有许多属性。在数值型关联规则挖掘（NARM）中，当使用处理连续数值型数据值的关联规则时，很难精确地提取出关注数值型数据统计分布的规则。所提出的方法，使快速发现的规则的数量所需的预测目的，使用进化计算的网络结构和策略，以池的解决方案，在整个世代。这有效地找到了属性组合，其中两个连续的感兴趣的变量所取的值都是窄范围的，并且可以在短时间内解决基于实例的二维回归问题。作为评估实验，执行使用与地图数据相关联的音乐数据的预测任务，并且设置灵活规则的发现条件。该方法在基于实例的回归问题中实现了较高的覆盖率，在NARM中基于统计分布的规则发现中是有效的。
In this paper, we propose a rule discovery method that can reveal a combination of attributes that provide characteristic distribution of two consecutive variables of interest directly at high speed in a database having many attributes. In numerical association rule mining (NARM), when using association rules that handle consecutive numerical data values, it is difficult to heuristically extract rules that focus on statistical distributions of numerical data. The proposed method enables quick discovery of the number of rules necessary for prediction purposes using evolutionary calculations characterized by a network structure and a strategy to pool solutions throughout generations. This effectively finds attribute combinations in which the values taken by two consecutive variables of interest are both narrow ranges and can address instance-based two-dimensional regression problems in a short time. As an evaluation experiment, a prediction task using musical data linked with map data was carried out, and the discovery condition of the flexible rule was set. This resulted in realizing a high coverage rate in the instance-based regression problem, and the proposed method was effective in rule discovery based on the statistical distribution in NARM.