Time Series Clustering Method Based on Principal Component Analysis
- DOI
- 10.2991/icimm-15.2015.163How to use a DOI?
- Keywords
- time series dataset; clustering method; principal component analysis (PCA); Euclidean distance
- Abstract
In terms of existing time series clustering method based on Euclidean distance metric, with the increasing dimension of time series, the time complexity of the algorithm will be increased too; and this method can also lead to incorrect clustering result because of it unable to recognize the abnormal values in time series. Principal component analysis retains large variance and contains more information by linear transformation; it can effectively reduce the dimension of the time series and identify outliers. This paper proposes the idea of time series clustering analysis method based on principal component analysis. Firstly, applying principal component analysis to time series dataset, by way of dimension reduction, obtained the corresponding coefficient matrix and eigenvalues. Secondly, using clustering method based on Euclidean distance on the calculated coefficient matrix, the clustering result of coefficient matrix is consistent with time series dataset. Using simulation data and meteorological data to validate this method, the experimental results show that time complexity of time series clustering method proposed in this paper is significantly better than the algorithm based on Euclidean distance, especially for time series dataset which has linear correlation.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Danyang Cao AU - Yuan Tian AU - Donghui Bai PY - 2015/07 DA - 2015/07 TI - Time Series Clustering Method Based on Principal Component Analysis BT - Proceedings of the 5th International Conference on Information Engineering for Mechanics and Materials PB - Atlantis Press SP - 888 EP - 895 SN - 2352-5401 UR - https://doi.org/10.2991/icimm-15.2015.163 DO - 10.2991/icimm-15.2015.163 ID - Cao2015/07 ER -