CAJ | 학술논문

时间序列的聚类算法是分析预测互联网搜索对象搜索指数和社交网络话题热度随时间变化趋势的重要过程,但目前时间序列聚类算法的研究存在两点不足:首先国内外的时间序列聚类的研究都采用等长划分的时间序列,这往往会丢失许多重要特征点,对数据挖掘结果产生一定的负面影响;其次直接使用时间序列观测值不能准确地度量时间序列的形状相似度.因此,通过标准分数z_score预处理消除了时间序列观测值数量级差异的影响,并设计了基于滑窗的不等长时间序列STS(short time series)距离和类k-means聚类算法的中心曲线计算方法,最终提出了基于滑窗不等长时间序列STS距离的聚类算法,从而解决了不等长时间序列聚类问题.采集互联网上的真实数据集作为测试样本,并进行了大量实验.实验结果表明,基于滑窗不等长时间序列STS距离的聚类算法不仅消除了时间序列观测值数量级差异的影响,解决了不等长时间序列聚类问题,并且比现有算法取得了更优的聚类效果.
시간서렬적취류산법시분석예측호련망수색대상수색지수화사교망락화제열도수시간변화추세적중요과정,단목전시간서렬취류산법적연구존재량점불족:수선국내외적시간서렬취류적연구도채용등장화분적시간서렬,저왕왕회주실허다중요특정점,대수거알굴결과산생일정적부면영향;기차직접사용시간서렬관측치불능준학지도량시간서렬적형상상사도.인차,통과표준분수z_score예처리소제료시간서렬관측치수량급차이적영향,병설계료기우활창적불등장시간서렬STS(short time series)거리화류k-means취류산법적중심곡선계산방법,최종제출료기우활창불등장시간서렬STS거리적취류산법,종이해결료불등장시간서렬취류문제.채집호련망상적진실수거집작위측시양본,병진행료대량실험.실험결과표명,기우활창불등장시간서렬STS거리적취류산법불부소제료시간서렬관측치수량급차이적영향,해결료불등장시간서렬취류문제,병차비현유산법취득료경우적취류효과.
Time series clustering is an important algorithm widely used by many applications, such as the analysis and forecast of topics on social media and search words on search engine. However, existing time series clustering algo-rithms suffer from two shortcomings. Firstly, time series clustering algorithms mostly work only for isometric time series with equal length, leading to the loss of many important features and negative impact of clustering results. Secondly, time series similarity metrics are not able to compare the shape similarity of time series. To address the problems, this paper proposes a novel computation framework to cluster time series data with non-equal length. At first, this paper uses z_score standardization to normalize the observed values of time series data. Next, based on sliding window, this paper extends STS (short time series) distance and designs a new distance measure for time series with non-equal time length. After that, this paper adapts the classic k-means algorithm to develop a new clustering algorithm. The extensive experimental results, by two real datasets that are collected from search engines and public data, success-fully verify that the proposed time series clustering algorithm can handle non-equal time series data and outperform the state of arts in terms of clustering accuracy and quality.