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Activity Number:
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112
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #308756 |
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Title:
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Mining Semantic Co-Location Patterns with Clustering Techniques
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Author(s):
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Bin Zhang*+ and Wen Jun Yin and Jin Dong and Ming Xie
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Companies:
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IBM China Research Laboratory and IBM China Research Laboratory and IBM China Research Laboratory and IBM China Research Laboratory
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Address:
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Building 19 Zhongguancun Software Park, Beijing, 100094, China
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Keywords:
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co-location mining ; semantic co-location mining ; spatial mining
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Abstract:
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Spatial co-location mining technologies are usually leveraged in broad applications to find subsets of spatial features frequently located together in spatial proximity, e.g. the frontage roads and highways in metropolitan road maps, and co-located services frequently requested together from mobile devices in location-based services. Traditional co-location mining approaches mainly focus on spatial attributes but consider less the internal attributes of objects. In this paper, we define a semantic-based interestingness measure to take into account internal attributes and extend the pure spatial co-location pattern to semantic co-location pattern. A clustering-based co-location pattern mining approach is also developed to discover those semantic co-location patterns. In the experiment, a real-world case study shows the proposed approach can effectively find semantic co-locations.
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