論文

査読有り
2020年9月

Improving Association Rule Mining for Infrequent Items Using Direct Importance Estimation

The 7th International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA 2020)
  • Toshiki Aoba
  • ,
  • Masato Kikuchi
  • ,
  • Mitsuo Yoshida
  • ,
  • Kyoji Umemura

記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1109/ICAICTA49861.2020.9429037

This paper proposes a method to find association rules for infrequent items. Despite the long history of association rule mining, infrequent items have been usually ignored. Recently, owing to the online nature of most systems, tackling infrequent items has become increasingly important to find emerging information. The proposed method not only has a sound theoretical background but is an exact solution of error minimization. Although highly similar to the standard method, Apriori, the solution uses a different formula than Apriori. Moreover, it consistently outperforms Apriori.

リンク情報
DOI
https://doi.org/10.1109/ICAICTA49861.2020.9429037
ID情報
  • DOI : 10.1109/ICAICTA49861.2020.9429037

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