Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
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)
Parsons, S.: Current approaches to handling imperfect information in data and knowledge bases. IEEE Trans. Knowledge Data Eng., 353–372 (1996)
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)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Q., Liao, S.S., Li, D. (2006). A Clustering Model for Mining Consumption Patterns from Imprecise Electric Load Time Series Data. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_152
Download citation
DOI: https://doi.org/10.1007/11881599_152
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45916-3
Online ISBN: 978-3-540-45917-0
eBook Packages: Computer ScienceComputer Science (R0)