An Analysis of Entry and Exit Data in Office by Decision Tree Learning Using Clustering Factor Matrix from Non-negative Multiple Matrix Factorization

Abstract

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.