Proposed dynamic clustering algorithm has strong robustness in clustering of time series multi-dimensional data.
本文算法对于时序数据的聚类具有较强的鲁棒性。
A dynamic clustering algorithm was proposed based on consistent matrix of dependent function for time series multi-dimensional data.
根据时序立体数据的特点，提出了基于关联函数一致性矩阵的动态聚类算法。
As the digitalization technology and database technology advanced recent years, data mining techniques that focus on multi-dimensional time series attracts more and more researchers.
近年来，随着数据库技术以及数字化技术的不断进步，针对高维时间序列的数据挖掘研究引起了越来越多学者广泛的兴趣。
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