基于不同聚类算法的高效轨迹代表生成移动web数据预测

V. Mishra, Megha Mishra, B. Dewangan, T. Choudhury
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引用次数: 1

摘要

重点介绍了移动和轨迹目标聚类(MOTRACLUS)算法,分析了基于web数据移动的子轨迹和实轨迹算法,提出了一种新的移动元素方法。本文对恰蒂斯加尔邦地区移动数据的轨迹对象的飓风数据测度和无质量数据测度熵进行了评价。本文介绍了用距离聚类最小描述长度(MDL)算法和其他相应的距离聚类(CLSTR)算法生成预测。重点介绍了最小聚类截面k近邻算法(LCSS)模型和MDL算法的维数欧几里德。该算法分为划分阶段和分组阶段。本文发展和改进了一组轨迹目标,并计算了运动目标的实际距离。该算法基于CLSTR算法,计算目标的运动轨迹。其中,作者通过考虑启发式参数来评估运动物体的熵。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Efficient Trajectory Representative Generation Moving Web-Based Data Prediction Using Different Clustering Algorithms
This paper highlighted moving and trajectory object cluster (MOTRACLUS) algorithm and analyzed the sub-trajectories and real-trajectories algorithm for moving web-based data and suggested a new approach of moving elements. This paper evaluates the hurricane data measure and mass less data measure entropy of trajectories objects of moving data of Chhattisgarh location. The paper covered prediction generation with their distance cluster minimum description length (MDL) algorithm and other corresponding distance cluster (CLSTR) algorithm. This paper highlighted the k-nearest algorithm with least cluster section (LCSS) model and dimensional Euclidean of MDL algorithm. The algorithm consists of two parts, that is, partitioning and grouping phase. This paper develops and enhances a cluster of trajectory objects and calculates the actual distance of moving objects. This algorithm works on the CLSTR algorithm and calculates trajectory movement of the object. In this, the authors evaluate the entropy of moving objects by consideration of the heuristic parameter.
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