Evolutionary Method for Two-dimensional Associative Local Distribution Rule Mining
Kaoru Shimada, Takaaki ARAHIRA, Shogo Matsuno · 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) · 2021
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.