Intelligent Detection of Office Occupancy Based on Hybrid Data Mining

Firstly, K-means clustering is carried out on the original data after pre-processing by fusing images, sensors, and behavioral data, and the data is divided into different clusters. Secondly, the Deep Residual Attention Network (DRAN) is utilized to extract the feature from the data of each cluster.

Finally, the Weighted Voting-based Extreme Learning Machine (WV-ELM) is employed to classify and detect the data, to realize the occupancy status identification with higher precision. The results show that the proposed model’s accuracy achieves 96.17%, at least an improvement of 6.23%. When iteration times are 100, precision, F1 score, and recall reach 91.30%, 90.96%, and 89.22%, respectively.