Fuzzy clustering algorithms based on fast dynamic time warping
LIU Yongli
WU Shuai
YANG Lishen
Abstract：In order to measure the similarity between pair-wise time series data rapidly and accurately,fast dy-namic time warping ( FDTW) distance is introduced,and the fuzzy C Means algorithm and fuzzy C medoids al-gorithm are both improved. The FDTW distance,which synchronizes time series by stretching or compressing data and can measure time series accurately even if they are asynchronous,has great advantage over the DTW distance in improving the computational efficiency. Experimental results show that the proposed algorithms could achieve the clustering precision.
Keywords：fuzzy clusteringfast dynamic time warpingcomputational efficiency
Publication Date：2017-11-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 111-116 )