Clustering bus load curves

This paper proposes the clustering of a set of busses through a fuzzy c-means clustering approach. The utilization of fuzzy techniques in a clustering problem aims the attainment of a partition fuzzy in the data set, allowing degrees of relationship between different elements of the set, this way, an element can belong to more than one group with different membership value. In this paper the clustering algorithm aims at exploring data characteristics and determining groups composed by busses with similar bus daily load. Results show the efficiency of the clustering method, where the data was classified into distinct groups such as: commercial, residential and industrial consumption profiles.

[1]  Jiawei Han,et al.  Mining Segment-Wise Periodic Patterns in Time-Related Databases , 1998, KDD.

[2]  A. Geva.  Non-stationary time-series prediction using fuzzy clustering , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[3]  Abraham Kandel,et al.  Knowledge discovery in time series databases , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[4]  E. Backer,et al.  Computer-assisted reasoning in cluster analysis , 1995 .

[5]  W. Peizhuang.  Pattern Recognition with Fuzzy Objective Function Algorithms (James C. Bezdek) , 1983 .

[6]  Sankar K. Pal,et al.  Fuzzy models for pattern recognition , 1992 .

[7]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[8]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[9]  Jiawei Han,et al.  Efficient mining of partial periodic patterns in time series database , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[10]  Isak Gath,et al.  Unsupervised Optimal Fuzzy Clustering , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Kyong Joo Oh,et al.  An intelligent clustering forecasting system based on change-point detection and artificial neural networks: application to financial economics , 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.