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.
KeywordsCluster Center Cluster Model Consumption Pattern Mining Pattern Interval Number
Unable to display preview. Download preview PDF.
- 1.Rodrigues, F., Duarte, J., Figueiredo, V., Vale, Z.A., Cordeiro, M.: A comparative analysis of clustering algorithms applied to load profiling. In: MLDM, pp. 73–85 (2003)Google Scholar
- 2.Keogh, E.J., Kasetty, S.: On the need for time series data mining benchmarks: A survey and empirical demonstration. In: Proc. 8th ACM SIGKDD Int. Conf. Knowledge Discovery Data Mining, pp. 102–111 (2002)Google Scholar
- 3.Parsons, S.: Current approaches to handling imperfect information in data and knowledge bases. IEEE Trans. Knowledge Data Eng., 353–372 (1996)Google Scholar
- 4.Liao, S.S., Tang, T.H., Liu, W.Y.: Finding relevant sequences in time series containing crisp, interval, and fuzzy interval data. IEEE Tran. Syst. Man, Cybern. B, 2071–2079 (2004)Google Scholar
- 5.Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)Google Scholar