@article{oai:ipsj.ixsq.nii.ac.jp:00174257,
 author = {岩沼, 宏治 and 佐生, 隼一 and 黒岩, 健歩 and 山本, 泰生 and Koji, Iwanuma and Shunichi, Sasho and Yasuho, Kuroiwa and Yoshitaka, Yamamoto},
 issue = {8},
 journal = {情報処理学会論文誌},
 month = {Aug},
 note = {本論文では負の相関ルール集合の圧縮形式について考察する．まず初めに，頻出アイテム集合の圧縮表現としてよく知られる飽和集合が，負の相関ルール集合の圧縮技術としては不適切であることを示す．次に，その解決策として極小生成子を用いた負ルール集合の圧縮表現を新しく提案する．極小生成子は飽和アイテム集合と対をなす概念である．提案した圧縮表現の無損失性などを理論的に証明する．また実証実験を行った結果，稠密なデータセット上の負ルール集合の圧縮に有効であることが確認できたので報告する．, In this paper, we study a lossless compression for a set of negative association rules. First, we show a representation difficulty which occurs in applying the closed itemset technique for such a compression problem. Next we propose a new compression method for a set of negative rules, which is based on minimal generators. A minimal generator for an itemset is a dual concept of a closed itemset, and can solve the above representation problem. We prove that the proposed method based on minimal generators is a lossless compression, and also show, through experiments, that the method is effective for compressing a set of negative rules extracted from dense data sets.},
 pages = {1845--1849},
 title = {負の相関ルール集合の極小生成子に基づく圧縮表現},
 volume = {57},
 year = {2016}
}