Evolutionary computation method to discover statistically characteristic itemsets
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- SHIMADA Kaoru
- Gunma University
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- MATSUNO Shogo
- Gunma University
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- ARAHIRA Takaaki
- Kyushu Institute of Information Sciences
Bibliographic Information
- Other Title
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- 統計的に特徴的な背景を持つアイテムセットを発見するための進化計算方法
Abstract
<p>We propose a method for discovering combinations of attributes (itemsets) against a background of statistical characteristics without obtaining frequent itemsets. The method uses evolutionary computations characterized by a network structure and a strategy to pool solutions over generations. The method directly discovers combinations of attributes such that a high correlation is observed between two continuous value variables from a database consisting of a large number of attributes as explanatory variables and two continuous value variables as objects of interest for their statistical properties. The proposed method, which seeks to achieve the discovery of small groups with statistical backgrounds from large data sets, extends the concept of frequent itemsets and provides a basis for generalizing the association rule representation.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2022 (0), 2G4GS204-2G4GS204, 2022
The Japanese Society for Artificial Intelligence
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Keywords
Details 詳細情報について
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- CRID
- 1390855656024612096
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- Text Lang
- ja
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- Data Source
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- JaLC
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- Abstract License Flag
- Disallowed