Open AccessCluster analysis of time series K-Means line loss based on DWT distance
Author(s) -
Liming Song,
Zhimin Chen,
Xianglong Meng,
Shichang Kang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2137/1/012063
Subject(s) - silhouette , line (geometry) , cluster analysis , cluster (spacecraft) , computer science , series (stratigraphy) , field (mathematics) , data mining , k means clustering , index (typography) , pattern recognition (psychology) , artificial intelligence , mathematics , paleontology , geometry , pure mathematics , biology , programming language , world wide web
This paper constructs an indicator system composed of inherent attributes and time characteristics of the line based on the line loss, and proposes a K-Means line loss cluster analysis model based on this indicator system. The line is classified according to the clustering results. The result is 314.51 on the CH index (Calinski Harabasz Index), 0.19 on the Silhouette Cofficient (Silhouette Cofficient), and a running time of 0.508s. Compared with the traditional algorithm, it is greatly improved. The field of line loss analysis has guiding significance.