CAJ | 학술논문

通过高维时间序列分割可以创建高级符号表示.提出一种针对高维时间序列的无监督分割算法,用于解决高维数据符号化的预处理问题.该算法实现对高维数据的聚类,应用最大熵投票模型进行序列分割.实验结果表明,其平均查全率和查准率分别为0.86和0.88,且整体性能优于主成分分析算法和概率主成分分析算法.
통과고유시간서렬분할가이창건고급부호표시.제출일충침대고유시간서렬적무감독분할산법,용우해결고유수거부호화적예처리문제.해산법실현대고유수거적취류,응용최대적투표모형진행서렬분할.실험결과표명,기평균사전솔화사준솔분별위0.86화0.88,차정체성능우우주성분분석산법화개솔주성분분석산법.
Through the high-dimension segmentation, the high-level symbol expression can be created. This paper proposes an unsupervised segmentation algorithm for high-dimension time series. This method can solve the pretreatmant problem of high-dimension symbolization. It realizes the clustering of high-dimension data, and uses max entropy voting model to do series segmentation. Experimental results show that the algorithm's average recall ratio and precision ration are respectively 0.86 and 0.88. Its whole performance is better than Principal Component Analysis(PCA) algorithm and Probability Principal Component Analysis(PPCA) algorithm.