Discovering personally meaningful places: An interactive clustering approach
Discovering personally meaningful places: An interactive clustering approach
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DOI：
10.1145/1247715.1247718
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发表时间：
2007-07
期刊：
影响因子：
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通讯作者：
Changqing Zhou;Dan Frankowski;Pamela J. Ludford;S. Shekhar;L. Terveen
中科院分区：
文献类型：
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作者：
Changqing Zhou;Dan Frankowski;Pamela J. Ludford;S. Shekhar;L. Terveen
发现一个人的有意义的地方涉及到获得一个人的日常生活和日常生活中重要的地方的物理位置及其标签。这个问题是由新兴的位置感知应用程序的需求驱动的，这些应用程序允许用户提出查询并获取有关地点的信息，例如，“家”、“工作”或“西北健身俱乐部”。由于缺乏对真正用户个人有意义的地方的构成的理解，从物理位置到个人有意义的地方的地图是一个挑战。之前的工作已经探索了从位置数据中发现个人地点的算法。然而，我们还不知道对这些算法进行系统的经验评估，这使得位置感知应用的设计者对他们的选择一无所知。我们的工作弥补了这种情况。我们扩展了一种发现地点的聚类算法。我们还定义了一套基本的评估指标和一个互动的评估框架。然后，我们进行了一个大规模的实验，收集了真实用户的位置数据和个人有意义的地方，并说明了我们的评估框架的实用性。我们的结果建立了一个基线，未来的工作可以参照它来衡量自己。它们还证明了我们的算法能够以合理的精度发现位置，并且性能优于著名的K-均值聚类位置发现算法。最后，我们提供的证据表明，需要比“点”更复杂的形状来代表人们日常生活中的所有地方。
The discovery of a person's meaningful places involves obtaining the physical locations and their labels for a person's places that matter to his daily life and routines. This problem is driven by the requirements from emerging location-aware applications, which allow a user to pose queries and obtain information in reference to places, for example, “home”, “work” or “Northwest Health Club”. It is a challenge to map from physical locations to personally meaningful places due to a lack of understanding of what constitutes the real users' personally meaningful places. Previous work has explored algorithms to discover personal places from location data. However, we know of no systematic empirical evaluations of these algorithms, leaving designers of location-aware applications in the dark about their choices. Our work remedies this situation. We extended a clustering algorithm to discover places. We also defined a set of essential evaluation metrics and an interactive evaluation framework. We then conducted a large-scale experiment that collected real users' location data and personally meaningful places, and illustrated the utility of our evaluation framework. Our results establish a baseline that future work can measure itself against. They also demonstrate that that our algorithm discovers places with reasonable accuracy and outperforms the well-known K-Means clustering algorithm for place discovery. Finally, we provide evidence that shapes more complex than “points” are required to represent the full range of people's everyday places.