图 书 馆
时序数据的时间骨架化：模式、分类和可视化Temporal Skeletonization on Sequential Data: Patterns, Categorization, and Visualization |
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| 课程网址: | http://videolectures.net/kdd2014_liu_sequential_data/ |
| 主讲教师: | Chuanren Liu |
| 开课单位: | 新泽西州立大学 |
| 开课时间: | 2014-10-07 |
| 课程语种: | 英语 |
| 中文简介: |  顺序模式分析的目标是找到在统计上相关的时间结构，在这些时间结构中按顺序传递值。随着现实世界动态场景的复杂性越来越高，通常需要越来越多的符号来编码有意义的序列。这就是所谓的“基数诅咒”，它会在计算效率和实际使用方面对顺序分析方法的设计提出重大挑战。确实，鉴于庞大的规模和顺序数据的异构性质，需要新的愿景和策略来应对挑战。为此，在本文中，我们提出了一种“时间框架化”方法来主动减少序列的表示，以发现重要的，隐藏的时间结构。关键思想是总结无向图中的时间相关性。然后，图的“骨架”用作更高的粒度，在该粒度上，更有可能识别隐藏的时间模式。同时，图的嵌入拓扑使我们能够将丰富的时间内容转换为度量空间。这为探索，量化和可视化顺序数据开辟了新的可能性。我们的方法已经显示出可以极大地减轻基数对顺序模式挖掘和聚类的挑战性的困扰。对企业对企业（B2B）营销应用程序的评估表明，我们的方法可以有效地从嘈杂的客户事件数据中发现关键的购买路径。 |
| 课程简介: | Sequential pattern analysis targets on finding statistically relevant temporal structures where the values are delivered in a sequence. With the growing complexity of real-world dynamic scenarios, more and more symbols are often needed to encode a meaningful sequence. This is so-called 'curse of cardinality', which can impose significant challenges to the design of sequential analysis methods in terms of computational efficiency and practical use. Indeed, given the overwhelming scale and the heterogeneous nature of the sequential data, new visions and strategies are needed to face the challenges. To this end, in this paper, we propose a 'temporal skeletonization' approach to proactively reduce the representation of sequences to uncover significant, hidden temporal structures. The key idea is to summarize the temporal correlations in an undirected graph. Then, the 'skeleton' of the graph serves as a higher granularity on which hidden temporal patterns are more likely to be identified. In the meantime, the embedding topology of the graph allows us to translate the rich temporal content into a metric space. This opens up new possibilities to explore, quantify, and visualize sequential data. Our approach has shown to greatly alleviate the curse of cardinality in challenging tasks of sequential pattern mining and clustering. Evaluation on a Business-to-Business (B2B) marketing application demonstrates that our approach can effectively discover critical buying paths from noisy customer event data. |
| 关 键 词: | 时序数据; 序列编码 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-16：zyk |
| 最后编审: | 2020-12-16：zyk |
| 阅读次数: | 70 |