A clustering model for mining consumption patterns from imprecise electric load time series data

This paper presents a novel clustering model for mining patterns from imprecise electric load time series. The model consists of three components. First, it contains a process that deals with representation and preprocessing of imprecise load time series. Second, it adopts a similarity metric that uses interval semantic separation (Interval SS)-based measurement. Third, it applies the similarity metric together with the k-means clustering method to construct clusters. The model gives a unified way to solve imprecise time series clustering problem and it is applied in a real world application, to find similar consumption patterns in the electricity industry. Experimental results have demonstrated the applicability and correctness of the proposed model. © Springer-Verlag Berlin Heidelberg 2006.