A Fast Clustering Algorithm for Power Data
DOI: 10.23977/poweet.2017.11007 | Downloads: 22 | Views: 2579
Shuqin Zeng 1, Haizhou Du 1, Tingting Dou 1
1 School of Computer Science and Technology, Shanghai University of Electric Power
Corresponding AuthorShuqin Zeng
Energy conservation is an urgent issue to solve on a global scale. A more and more widely used method for energy saving and emission reduction is the applications of data mining technology including data clustering in power system. However, power data has characteristics of large volume, high dimensions, discrete and complex datasets which lead to poor clustering results when we choose common classic clustering algorithm. In our paper, we proposed D-CFSFDP algorithm which is suitable for power data clustering. We do experiments compared with DBSCAN algorithm and K-means algorithm. We demonstrate the power of the algorithm on the power data from Shanghai Energy Conservation Supervision Center.
KEYWORDSPower data clustering, Density-based Clustering, Energy saving.
CITE THIS PAPER
Shuqin, Z. , Haizhou, D. and Tingting,D. A Fast Clustering Algorithm for Power Data. International Journal of Power Engineering and Engineering Thermophysics (2017) 1: 40-46.
 Chao Jing,Tianlong Guet al,“An Energy-Saving Clustering Algorithm Based on LEACH”,Knowledge Acquisition and Modeling Workshop,2008,DOL:10.1109/2008.4810509.
 J. Han and M. Kamber, “Data mining: Concepts and techniques,“Data Mining Concepts Models Methods and Algorithms Second Edition, vol. 5, no. 4, pp. 1 – 18, 2006.
 Alex Rodriguez, Alessandro Laio, “Clustering by fast search and find of density peaks”, Vol.344,no.6191,pp.1492-1496, Science 27, June 2014.
 Rongfang Bie, Rashid Mehmood, Shanshan Ruan, Yunchuan Sun et al, “Adaptive fuzzy clustering by fast search and find of density peaks”, Volume 20, Issue 5, pp 785–793, October 2016.
 Zhang WenKai, Li Jing, “Extended fast search clustering algorithm：Widely density cluster,no density peaks”.
 Shihua Liu,Bingzhong Zhou et al,“Clustering Mixed Data by Fast Search and Find of Density Peaks”, Mathematical Problems in Engineering, Volume 2017 (2017), Article ID 5060842, 7 pages.
 P. Berkhin, “A survey of clustering data mining techniques,” in Grouping Multidimensional Data, J. Kogan, C. Nicholas, and M. Teboulle, Eds.Springer Berlin Heidelberg, 2006, pp. 25–71.
 Jimeng Sun, Fei Wang, Jianying Hu, Shahram Edabollahi,“Supervised Patient Similarity Measure of Heterogeneous Patient Records”,Volume 14, Issue 1, June 2012.
 Emre Karakoc, Artem Cherkasov, S. Cenk Sahinalp ,”Novel Approaches for Small Biomolecule Classification and Structural Similarity Search”, Sigkdd , Volume 9, Issue 1, June 2007.
 M. Ester, H.-P. Kriegel, J. Sander, X. Xu, in Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, E. Simoudis, J. Han, U. Fayyad, Eds. (AAAI Press, Menlo Park, CA, 1996), pp. 226–231.
 Y. Cheng, Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17, 790 (1995). CrossRefWeb of Science Search Google Scholar .
 A. Gionis, H. Mannila, P. Tsaparas, Clustering aggregation. ACM Trans. Knowl. Discovery Data 1, 4 , es (2007). CrossRef Search Google Scholar .
 P. Fränti, O. Virmajoki, Iterative shrinking method for clustering problems. Pattern Recognit. 39, 761–775 (2006). CrossRefWeb of Science Search Google Scholar .
 Biant D,Kut A(2007).“ST-DBSCAN:An algorithm for clustering spatial-temporal data.”Data and Knowledge Engineering,60(1),208-221.