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Discovering personally meaningful places: An interactive clustering approach

Published: 01 July 2007 Publication History

Abstract

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.

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Fazli Can

Location-aware systems provide information services according to a user's current location and the personal importance of that location to him or her. In this paper, the authors introduce a clustering algorithm using an R-tree index for discovering personally meaningful places. Their approach produces a partitioning structure and is deterministic (that is, it always produces the same output). It is shown that determining the algorithm's parameters is not difficult; furthermore, the results produced are stable for different values. The paper provides the details of the experimental data collection procedure and the interactive evaluation framework, and presents the experimental results. The authors show that their approach outperforms the k -means clustering algorithm for place discovery. The paper is easy to understand, but has presentation flaws. The "Related Work" section and the "Prior Work" appendix are related, but separated from each other for no explainable reason. Sections 5.1 and 5.2 contain repetitious material. Section 3.1 contains a forward reference to Section 2.1 that may confuse readers. The authors claim that their results provide a baseline for future work of comparison purposes. Without using the same experimental data, the validity of this claim is disputable. However, the paper is useful for researchers working in this field, especially with regard to its experimental design. I think it will be inspirational to researchers working on the discovery of interesting locations in other contexts, such as user interfaces and Web browsing. Extending such studies from an individual to a community, in the implementation of location recommender systems and the like, is another inviting challenge. Online Computing Reviews Service

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Published In

cover image ACM Transactions on Information Systems
ACM Transactions on Information Systems  Volume 25, Issue 3
July 2007
150 pages
ISSN:1046-8188
EISSN:1558-2868
DOI:10.1145/1247715
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 2007
Published in TOIS Volume 25, Issue 3

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Author Tags

  1. Ubiquitous computing
  2. clustering algorithms
  3. field studies
  4. location-aware applications
  5. place discovery

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