Research on improved fuzzy mean curve clustering method based on two-scale measurement

doi:10.3969/j.issn.2097-0706.2022.04.001

Abstract

Abstract:

There are many functional data showing obvious curve features that vary with time in intelligent power networks. Curve clustering can effectively mine the data information. Aiming at the difficulty in selecting the initial clustering centre for fuzzy mean clustering algorithm and the inaccurate similarity measurement of curve clustering methods, an improved fuzzy mean curve clustering method based on two-scale metric is proposed. The longitudinal shape similarity of a curve is measured according to the Pearson distance, and the horizontal shape similarity of the curve is measured according to the dynamic time wrapping distance. Then,a density peak algorithm based on two-scale measurement is proposed to determine the initial clustering centre. The improved entropy weight method combines Pearson distance and dynamic time wrapping distance in similarity measurement of clustering algorithm. Clustering validity indexes are taken to evaluate the clustering results and algorithm performance from the aspects of clustering effect and algorithm stability. At last, taking the annual data of wind power outputs in a region as the example for clustering analysis,the results verify the correctness and effectiveness of the model and calculation method.

Key words: intelligent grid, data mining, curve clustering, improved fuzzy mean curve clustering, Pearson distance, dynamic time warping distance, improved entropy weight method, similarity, wind power

CLC Number:

TK01⁺1：TP301.6：TM614

CHEN Tiantian, GAO Yajing, LU Zhanhui. Research on improved fuzzy mean curve clustering method based on two-scale measurement[J]. Integrated Intelligent Energy, 2022, 44(4): 1-11.

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算法	第1类	第2类	第3类	第4类	第5类
标准FCM	7.271 2	5.480 6	6.829 0	5.179 9	2.976 5
改进FCM	2.619 1	3.828 7	3.546 8	3.717 0	3.718 5

算法	聚类数	XB指标值	SI指标值
标准FCM	5	5.234 5	5.547 4
改进FCM	5	1.250 8	3.486 0

初始聚类中心选取方法	聚类数	XB	SI	最优聚类次数	程序平运行时间/s
随机选取	5	1.250 8	3.468 0	2	0.928 8
改进K-means	5	2.115 1	3.598 7	30	0.670 9
欧氏密度峰值	5	2.115 0	3.598 7	30	0.673 3
本文方法	5	1.250 8	3.468 0	30	0.845 2

相似度度量方法	最优聚类数	XB
欧氏距离	3	3.755 2
皮尔逊距离	2	1.556 0
DTW距离	2	3.799 9
综合距离	5	1.250 8

相似度度量方法	聚类数	XB	SI	程序平均运行时间/s
欧氏距离	5	5.234 5	5.547 5	0.636 6
皮尔逊距离	5	1.586 0	3.644 0	0.689 4
DTW距离	5	5.777 2	4.881 1	1.171 2
综合距离	5	1.250 8	3.468 0	0.845 2