On the Relationships between Clustering and Spatial Co-location Pattern Mining

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On the Relationships between Clustering and Spatial Co-location Pattern Mining

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by Yan Huang , Pusheng Zhang

Venue:	In Proceedings of the 18th IEEE international Conference on Tools with Artificial intelligence (November 13 - 15, 2006). ICTAI. IEEE Computer Society
Citations:	8 - 0 self

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@INPROCEEDINGS{Huang_onthe,
    author = {Yan Huang and Pusheng Zhang},
    title = {On the Relationships between Clustering and Spatial Co-location Pattern Mining},
    booktitle = {In Proceedings of the 18th IEEE international Conference on Tools with Artificial intelligence (November 13 - 15, 2006). ICTAI. IEEE Computer Society},
    year = {},
    pages = {513--522}
}

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Abstract

The goal of spatial co-location pattern mining is to find subsets of spatial features frequently located together in spatial proximity. Example co-location patterns include services requested frequently and located together from mobile devices (e.g., PDAs and cellular phones) and symbiotic species in ecology (e.g., Nile crocodile and Egyptian plover). Spatial clustering groups similar spatial objects together. Reusing research results in clustering, e.g. algorithms and visualization techniques, by mapping colocation mining problem into a clustering problem would be very useful. However, directly clustering spatial objects from various spatial features may not yield well-defined colocation patterns. Clustering spatial objects in each layer followed by overlaying the layers of clusters may not applicable to many application domains where the spatial objects in some layers are not clustered. In this paper, we propose a new approach to the problem of mining co-location patterns using clustering techniques. First, we propose a novel framework for co-location mining using clustering techniques. We show that the proximity of two spatial features can be captured by summarizing their spatial objects embedded in a continuous space via various techniques. We define the desired properties of proximity functions compared to similarity functions in clustering. Furthermore, we summarize the properties of a list of popular spatial statistical measures as the proximity functions. Finally, we show that clustering techniques can be applied to reveal the rich structure formed by co-located spatial features. A case study on real datasets shows that our method is effective for mining co-locations from large spatial datasets. 1This work was done when the second author was with the University

Keyphrases

spatial co-location pattern mining spatial object spatial feature proximity function symbiotic specie egyptian plover research result visualization technique new approach co-location mining rich structure second author group similar spatial object clustering problem continuous space cellular phone nile crocodile well-defined colocation pattern case study large spatial datasets real datasets co-location pattern co-located spatial feature novel framework various technique example co-location pattern various spatial feature spatial proximity popular spatial statistical measure many application domain desired property mobile device colocation mining problem

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