课程语种:
英语
中文简介:
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在一些应用领域，如手语、医学和传感器网络中，事件不一定是瞬时的，但它们可以有一段时间。基于间隔的事件序列可能包含有用的领域知识；因此，搜索、索引和挖掘此类序列至关重要。本文介绍了两种距离度量，用于比较基于区间的事件序列，这些事件序列可用于分类和聚类等多种数据挖掘任务。第一个度量将每个基于间隔的事件序列映射到一组包含所有并发事件信息的向量。然后使用现有的动态编程方法对这些集进行比较。第二种方法称为artemis，通过将两个序列映射成二部图来查找间隔之间的对应关系。通过匈牙利算法推断出相似性。此外，我们还提出了阿耳忒弥斯的线性时间下限。这两种方法的性能都是在三个领域的数据上进行测试的：手语、医学和传感器网络。实验表明，在高水平人工引入噪声的鲁棒性方面，卤虫具有优越性。
课程简介:
In several application domains, such as sign language, medicine, and sensor networks, events are not necessarily instantaneous but they can have a time duration. Sequences of interval-based events may contain useful domain knowledge; thus, searching, indexing, and mining such sequences is crucial. We introduce two distance measures for comparing sequences of interval-based events which can be used for several data mining tasks such as classification and clustering. The first measure maps each sequence of interval-based events to a set of vectors that hold information about all concurrent events. These sets are then compared using an existing dynamic programming method. The second method, called Artemis, finds correspondence between intervals by mapping the two sequences into a bipartite graph. Similarity is inferred by employing the Hungarian algorithm. In addition, we present a linear-time lowerbound for Artemis. The performance of both measures is tested on data from three domains: sign language, medicine, and sensor networks. Experiments show the superiority of Artemis in terms of robustness to high levels of artificially introduced noise.
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