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Fifth IEEE International Conference on Data Mining (ICDM'05)
Spatial Clustering of Chimpanzee Locations for Neighborhood Identification
Houston, Texas
November 27-November 30
ISBN: 0-7695-2278-5
Sandeep Mane, University of Minnesota
Carson Murray, University of Minnesota
Shashi Shekhar, University of Minnesota
Jaideep Srivastava, University of Minnesota
Anne Pusey, University of Minnesota
Since 1960, the chimpanzees (Pan troglodytes) of Gombe National Park, Tanzania, have been studied by behavioral ecologists, including Jane Goodall. Data have been collected for more than 40 years and are being analyzed by researchers in order to increase our understanding of the social structure of chimpanzees. In this paper, we consider the following question of interest to behavioral ecologists — "Does clustering exist among female chimpanzees in terms of their spatial locations?" The analysis of this question will help behavioral ecologists to learn about the space use and the social interactions between female chimpanzees. The data collected for this analysis are marked spatial point patterns over the park. Current spatial clustering methods lack the ability to handle such marked point patterns directly. This paper presents a novel application of spatial point pattern analysis and data mining techniques to the ecological problem of clustering female chimpanzees. We found that Ripley?s K-function provides a powerful statistical tool for evaluating clustering behavior among spatial point patterns. We then proposed two clustering approaches for marked point patterns using the K-function. Experimental results using the proposed clustering methods provide significant insight into the dynamics of female chimpanzee space use and into the overall social stucture of the species. In addition, the proposed methods can be extended to also include temporal information.
Citation:
Sandeep Mane, Carson Murray, Shashi Shekhar, Jaideep Srivastava, Anne Pusey, "Spatial Clustering of Chimpanzee Locations for Neighborhood Identification," icdm, pp.737-740, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005
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