文献类型：会议论文

英文题名：Clustering Multivariate Time Series from Large Sensor Networks

作者：Liang, Jianning[1];Zhou, Yan[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai, Peoples R China

会议论文集：International Conference on Computer Systems, Electronics and Control (ICCSEC)

会议日期：DEC 25-27, 2017

会议地点：Dalian, PEOPLES R CHINA

语种：英文

外文关键词：Multivariate Time Series; Texture Features; Chain Similarities

摘要：In recent years, as the sensor technology develops swiftly, a large number of sensors can be deployed to form a sensor network. Massive data are generated from large sensor networks. It becomes an interesting problem to cluster high-dimensional multivariate time series from large sensor networks to discover the hidden regularity. In this paper, we propose a new method to cluster the high-dimensional time series. In our scheme, the output of the sensors are treated as gray values of images. Then, new image features (BSF, Bipolar Sigmoid Feature) and the chain similarity are presented to measure the similarities of time series. Finally, the hierarchical clustering method is used to discover data patterns. The effectiveness of our method is evaluated on the Twin Cities traffic data. In the experimental results, it displays that there are three regular patterns in the weekday data and two patterns in the weekend day.