warp-kmeans

  传统的kmeans聚类算法不能保证时间序列聚类结果的时序性；基于warp-kmeans聚类算法可以有效处理时间序列的聚类问题，而且复杂度低，迭代次数少。 ，输出有目标函数在每次迭代后的值，聚类中心以及聚类区间。内有测试数据和相关文献，点击 test.m 可以完美运行(The traditional kmeans clustering algorithm can not guarantee the time series of the time series clustering results. The warp-kmeans clustering algorithm can effectively deal with the clustering problem of time series, and the complexity is low, and the number of iterations is less. The output has the value of the target function after each iteration, the clustering center and the clustering interval. There are test data and related documents, click test.m can be perfect operation.)

warp-kmeans\data.mat
warp-kmeans\test.m
warp-kmeans\warp_kmeans.m
warp-kmeans\【2013】（W-KM）Warped K-Means_ An algorithm to cluster sequentially-distributed data.pdf
warp-kmeans