Cross-Correlation Based Clustering And Dimension Reduction Of Multivariate Time Series
2017 IEEE 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT ENGINEERING SYSTEMS (INES)(2017)
摘要
In this paper, we investigate dimension reduction possibilities of multidimensional time series data and we introduce a graph based clustering approach using the cross-correlation between time series. The proposed solution consists of two main steps: introducing a novel similarity measure for measuring cross-correlations and a graph-based clustering technique. These two parts are both compared to existing techniques, including noise tolerance and our solution performs better in a noisy environment. The proposed solution is applied to performance metrics of a specific data processing system in order to identify and efficiently visualize connections among the collected metrics. The introduced method provides a more balanced clustering than classic ones, and it is suitable to reveal dependencies and connections among performance metrics time series data.
更多查看译文
关键词
data processing system,similarity measure,cross-correlation,multidimensional time series data,dimension reduction possibilities,multivariate time series,performance metrics time series data,balanced clustering
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要