CAJ | 학술논문

工业监控系统所采集到的多元时间序列在利用数据挖掘技术获取内部存在的未知模式的过程中，经常会出现原始数据庞杂、分段结果重复、交集过多和界限不清晰等问题，导致含有突变变量或数据间相关性差的数据集进行模式挖掘结果不理想。针对上述问题，本文提出了一种新的多元时序模糊聚类分段挖掘算法。实验结果表明，该算法克服了Gath-Geva算法聚类精度易受初始值影响的不足，能够较好地反映出原始数据中潜在的过程变化，从而有效地处理时间序列的分段问题并得到理想的挖掘结果。
공업감공계통소채집도적다원시간서렬재이용수거알굴기술획취내부존재적미지모식적과정중，경상회출현원시수거방잡、분단결과중복、교집과다화계한불청석등문제，도치함유돌변변량혹수거간상관성차적수거집진행모식알굴결과불이상。침대상술문제，본문제출료일충신적다원시서모호취류분단알굴산법。실험결과표명，해산법극복료Gath-Geva산법취류정도역수초시치영향적불족，능구교호지반영출원시수거중잠재적과정변화，종이유효지처리시간서렬적분단문제병득도이상적알굴결과。
Multivariate time series collected by industrial monitoring systems often have problems such as numerous raw data, repeated segmentation results, redundant intersections and blurry boundaries in the process of using data mining technologies to acquire internal existing unknown patterns, leading to unsatisfied mining results when the dataset involves mutation variables or inferior relevance among the data. To resolve these problems, this article introduces a new multiple time sequence clustering algorithm. Experi-mental results show that this algorithm can overcome the shortage that the accuracy of clustering is often affected by initial values in the Gath-Geva algorithm. It can exhibit the potential variation of raw data and thus efficiently deal with segmentation in multivariate time series to get ideal mining results.