[1E2-OS-3a] AI における離散構造処理と制約充足(1)

[1E2-OS-3a-04] An Analysis of Entry and Exit Data in Office by Decision Tree Learning Using Clustering Factor Matrix from Non-negative Multiple Matrix Factorization

〇Seidai Kojima¹, Hayato Ishigure¹, Miwa Sakata¹, Atsuko Mutoh¹, Koichi Moriyama¹, Nobuhiro Inuzuka¹ (1. Nagoya Institute of Technology)

Keywords:User behavior patterns , Non-negative Multiple Matrix Factorization (NMMF), Clustering, Decision Tree Learning , Entry and Exit Data

Recently, IC card systems are popular and their log data are used for analyzing human behaviors. In this paper, we extract user behavior patterns using Non-negative Multiple Matrix Factorization (NMMF) and propose an analysis method to analyze patterns and attribute information by decision tree learning using clustering factor matrix. We examine our proposed method using actual entry and exit data and confirm the effect.