A Clustering Model for Mining Consumption Patterns from Imprecise Electric Load Time Series Data

  • Qiudan Li
  • Stephen Shaoyi Liao
  • Dandan Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


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.


Cluster Center Cluster Model Consumption Pattern Mining Pattern Interval Number 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qiudan Li
    • 1
  • Stephen Shaoyi Liao
    • 2
  • Dandan Li
    • 3
  1. 1.Laboratory of Complex Systems and Intelligence Science, Institute of AutomationChinese Academy of SciencesBeijing
  2. 2.Department of Information System, City University of Hong Kong, School of Economics and ManagementSouth West Jiao Tong UniversityChina
  3. 3.Department of Automation and Computer-Aided EngineeringThe Chinese University of Hong Kong 

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