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Predicting Locations Using Map Similarity(PLUMS): A Framework for Spatial Data Mining Sanjay Chawla(Vignette Corporation) Shashi Shekhar, Weili Wu(CS, Univ. of Minnesota) Uygar Ozesmi(Ericyes University, Turkey) http://www.cs.umn.edu/research/shashi-group
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Outline Motivation Application Domain Distinguishing characteristics of spatial data mining Problem Definition Spatial Statistics Approach Our approach: PLUMS Experiments, Results, Conclusion and Future Work
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Motivation Historical Examples of Spatial Data Exploration –Asiatic Cholera, 1855 –Theory of Gondwanaland –Effect of fluoride on Dental Hygiene A potential application in news –Tracking the West Nile Virus
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Application Domain Wetland Management: Predicting locations of bird(red-winged blackbird) nests in wetlands Why we choose this application ? –Strong spatial component –Domain Expertise –Classical Data Mining techniques(logistic regression, neural nets) had already been applied
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Application Domain: Continued.. Nest Locations Distance to open water Vegetation DurabilityWater Depth
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Unique characteristics of spatial data mining Spatial Autocorrelation Property
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Unique characteristics…cont Average Distance to Nearest Prediction(ADNP):
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Location Prediction:Problem Formulation Given: A spatial framework S. – Explanatory functions, – Dependent function F –A family F of learning model function mappings Find an element Objective: maximize (map_similarity = classification_accuracy + spatial accuracy) Constraints: spatial autocorrelation exists
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Spatial Statistics Approach 1. 2. 2”Logistic Regression:
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Spatial Stat: Solution Techniques Least Square Estimation: Biased and Inconsistent Maximum Likelihood: Involve computation of large determinant(from W) Bayesian: Monte Carlo Markov Chain(e.g. Gibbs Sampling)
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Our Approach
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Experiment Setup
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Result(1)
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Result(2)
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Conclusion and Future work PLUMS >> Classical Data Mining techniques PLUMS State-of-the-art Spatial Statistics approaches Better performance(two orders of magnitude) Try other configurations of the PLUMS framework and formalize!
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