Extracting Daily Patterns of Human Activity Using Non-Negative Matrix Factorization

This paper presents an algorithm to mine basic patterns of human activities on a daily basis using non-negative matrix factorization (NMF). The greatest benefit of the algorithm is that it can elicit patterns from which meanings can be easily interpreted. To confirm its performance, the proposed algorithm was applied to PC logging data collected from three occupations in offices. Daily patterns of software usage were extracted for each occupation. Results show that each occupation uses specific software in its own time period, and uses several types of software in parallel in its own combinations. Experiment results also show that patterns of 144 dimension vectors were compressible to those of 11 dimension vectors without degradation in occupation classification performance. Therefore, the proposed algorithm compressed basic software usage patterns to about one-tenth of their original dimensions while preserving the original information. Moreover, the extracted basic patterns showed reasonable interpretation of daily working patterns in offices.