특징 요약을 통한 공기조화 시뮬레이션 데이터의 혼합형 다변량 시계열 군집화 품질 향상

초록

Existing approaches for multivariate time series data clustering analysis often result in significant information loss, thereby reducing both clustering performance and interpretability. Moreover, most existing techniques primarily focus on numerical variables, making them less effective for real-world datasets that often include both numerical and categorical variables. To address these problems, this paper proposes a novel clustering technique for mixed-type multivariate time series data, enhancing interpretability by summarizing the data into representative features. The proposed technique is fundamentally different from existing methods in that it summarizes features to cluster mixed-type multivariate time series data. We evaluated the proposed method against existing techniques using three clustering evaluation metrics on two HVAC simulation datasets (MZVAV-1 and MZVAV-2-1). Experimental results showed that the proposed method outperformed existing techniques in clustering quality for over 61% of metric–cluster count combinations on MZVAV-1, and over 40% on MZVAV-2-1. These findings confirmed that the proposed technique could significantly improve clustering performance and interpretability for mixed-type time-series data.

키워드