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This paper presents a novel clustering method with an ensemble distance metric to identify meaningful daily patterns of building indoor air quality (IAQ). The novelty of this work is to construct an ensemble of weighted distance metrics with weights being adaptive to the characteristics of IAQ data. The ensemble distance was constructed based on Euclidean distance, Pearson correlation coefficient and cross-correlation coefficient to capture absolute levels, overall trends, and volatilities of IAQ data. The weights for individual distance metrics are automatically adapted to the characteristics of IAQ data based on the Z-scores of the distances among all pair-wise samples in the dataset. In addition, IAQ daily time series are segmented into short sequences to reduce the computational cost. Two datasets containing one-week IAQ observations were used to verify the proposed method. The results showed that the proposed method is able to capture insightful information on the distribution and ....

Aily Patterns , Istance Metric , Nsemble Distance , Indoor Air Quality , K Means ,