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›› 2016,Vol. 31 ›› Issue (4): 649-660.doi: 10.1007/s11390-016-1654-2
所属专题： Data Management and Data Mining
• Special Section on Selected Paper from NPC 2011 • 上一篇 下一篇
Meng Chen1(陈勐), Xiaohui Yu1,2(禹晓辉), Member, CCF, IEEE, and Yang Liu1,*(刘洋), Member, CCF, IEEE
Meng Chen1(陈勐), Xiaohui Yu1,2(禹晓辉), Member, CCF, IEEE, and Yang Liu1,*(刘洋), Member, CCF, IEEE
下一个位置预测在很多基于位置的应用中起到重要作用。凭借坚实的理论基础,基于马尔科夫模型的方法在这个问题上取得了巨大的成功。在本文中,我们寻求通过理解对象间的相似度来提高预测效果。我们提出了一种新的加权马尔科夫模型,它在挖掘移动模式时同时考虑了位置序列和对象间的相似度。具体来说,我们首先为每个对象利用其历史轨迹训练一个马尔科夫模型,然后从两方面(空间位置相似度和轨迹相似度)来计算不同对象之间的相似度。最后,我们将相似度作为到达每一个候选下一个位置的概率的权重,并返回排名靠前的位置作为预测结果。我们在一个真实的数据集上进行了大量实验,实验结果显示我们提出的方法在预测准确度上比现有方法有很大提高。
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