@article{oai:ipsj.ixsq.nii.ac.jp:00147622,
 author = {Keisuke, Otaki and Akihiro, Yamamoto and Keisuke, Otaki and Akihiro, Yamamoto},
 issue = {1},
 journal = {情報処理学会論文誌数理モデル化と応用（TOM）},
 month = {Feb},
 note = {Finding periodic regularity in sequential databases is an important topic in Knowledge Discovery and in pattern mining such regularity is modeled as periodic patterns. Although efficient enumeration algorithms have been studied, applying them to real databases is still challenging because they are noisy and most transactions are not extremely frequent in practice. They cause a (combinatorial) pattern explosion and the difficulty of tuning a threshold parameter. To overcome these issues we provide a novel pre-processing method called skeletonization, which was recently introduced for finding sequential patterns. It tries to find clusters of symbols in patterns, aiming at shrinking the space of all possible patterns in order to avoid the combinatorial explosion by considering co-occurrences of symbols. Although the original method cannot allow for periods, we generalize it by using the periodicity. We give experimental results using both synthetic and real datasets to show the effectiveness of our approach, and compare results of mining with and without the skeletonization to see that our method is helpful for mining comprehensive patterns., Finding periodic regularity in sequential databases is an important topic in Knowledge Discovery and in pattern mining such regularity is modeled as periodic patterns. Although efficient enumeration algorithms have been studied, applying them to real databases is still challenging because they are noisy and most transactions are not extremely frequent in practice. They cause a (combinatorial) pattern explosion and the difficulty of tuning a threshold parameter. To overcome these issues we provide a novel pre-processing method called skeletonization, which was recently introduced for finding sequential patterns. It tries to find clusters of symbols in patterns, aiming at shrinking the space of all possible patterns in order to avoid the combinatorial explosion by considering co-occurrences of symbols. Although the original method cannot allow for periods, we generalize it by using the periodicity. We give experimental results using both synthetic and real datasets to show the effectiveness of our approach, and compare results of mining with and without the skeletonization to see that our method is helpful for mining comprehensive patterns.},
 pages = {33--42},
 title = {Periodic Pattern Mining with Periodical Co-occurrences of Symbols},
 volume = {9},
 year = {2016}
}