Pattern discovery from time series is of fundamental importance. Most of the algorithms of pattern discovery in time series capture the values of time series based on some kinds of similarity measures. Affected by the...Pattern discovery from time series is of fundamental importance. Most of the algorithms of pattern discovery in time series capture the values of time series based on some kinds of similarity measures. Affected by the scale and baseline, value-based methods bring about problem when the objective is to capture the shape. Thus, a similarity measure based on shape, Sh measure, is originally proposed, andthe properties of this similarity and corresponding proofs are given. Then a time series shape pattern discovery algorithm based on Sh measure is put forward. The proposed algorithm is terminated in finite iteration with given computational and storage complexity. Finally the experiments on synthetic datasets and sunspot datasets demonstrate that the time series shape pattern algorithm is valid.展开更多
基于地域的移动模式(zone-based movement pattern,ZMP)的发掘通过对出租车轨迹的聚类分析,同步发掘地域与移动轨迹。该方法通过ZMP的合并达到新地域发掘的目的,并加以距离和专题属性组成的相邻约束以保留移动的方向性、地域的功能属性...基于地域的移动模式(zone-based movement pattern,ZMP)的发掘通过对出租车轨迹的聚类分析,同步发掘地域与移动轨迹。该方法通过ZMP的合并达到新地域发掘的目的,并加以距离和专题属性组成的相邻约束以保留移动的方向性、地域的功能属性以及地域间的距离关系。通过连接矩阵迭代计算得到最优合并的ZMP进行合并,从而发掘ZMP,同时通过覆盖度、精准度以及基于这两者的平衡评估因子等对合并得到的ZMP进行评定。通过现实世界的出租车数据进行实验,结果表明该方法高效可行,能合理地实现合并现有区以发掘新地域。展开更多
文摘Pattern discovery from time series is of fundamental importance. Most of the algorithms of pattern discovery in time series capture the values of time series based on some kinds of similarity measures. Affected by the scale and baseline, value-based methods bring about problem when the objective is to capture the shape. Thus, a similarity measure based on shape, Sh measure, is originally proposed, andthe properties of this similarity and corresponding proofs are given. Then a time series shape pattern discovery algorithm based on Sh measure is put forward. The proposed algorithm is terminated in finite iteration with given computational and storage complexity. Finally the experiments on synthetic datasets and sunspot datasets demonstrate that the time series shape pattern algorithm is valid.
文摘基于地域的移动模式(zone-based movement pattern,ZMP)的发掘通过对出租车轨迹的聚类分析,同步发掘地域与移动轨迹。该方法通过ZMP的合并达到新地域发掘的目的,并加以距离和专题属性组成的相邻约束以保留移动的方向性、地域的功能属性以及地域间的距离关系。通过连接矩阵迭代计算得到最优合并的ZMP进行合并,从而发掘ZMP,同时通过覆盖度、精准度以及基于这两者的平衡评估因子等对合并得到的ZMP进行评定。通过现实世界的出租车数据进行实验,结果表明该方法高效可行,能合理地实现合并现有区以发掘新地域。
