Location Prediction and Spatial Data Mining (S. Shekhar)

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Presentation transcript:

Location Prediction and Spatial Data Mining (S. Shekhar) Specific Project in 2001-2002 Evaluation of location prediction techniques Towards high performance parallel implementation AHPCRC Relevance – Projectile Target Interaction Portfolio Increase lethality of weapons such as guided missiles Location prediction for map matching to check correctness of missile trajectory To identify unanticipated obstacle Towards possible rerouting Army Relevance in general Predicting global hot spots (FORMID) Army land management endangered species vs. training and war games Search for local trends in massive simulation data Critical infra-structure defense (threat assessment) Inferring enemy tactics (e.g. flank attack) from blobology Locating enemy (e.g. sniper in a haystack, sensor networks) Locating friends to avoid friendly fire

Location Prediction Problem Definition: Past Approaches: Given: 1. Spatial Framework 2. Explanatory functions: 3. A dependent function: 4. A family of function mappings: Find: A function Objective: maximize classification accuracy Constraints: Spatial Autocorrelation in dependent function Past Approaches: Non-spatial: logistic regression, decision trees, Bayesian Assume independent distribution for learning samples Auto-correlation => poor prediction performance Spatial: Spatial auto-regression (SAR), Markov random field Bayesian classifier (MRF) No literature comparing the two! Learning algorithms for SAR are slow (took 3 hours for 5000 data points)! Nest locations Distance to open water Vegetation durability Water depth

Accomplishments Formal Results Experimental results SAR - parametric statistics, provides confidence measures in model MRF from non-parametric statistics SAR : MRF-BC :: linear regression : Bayesian Classifier Rewrite SAR as y = (QX)  + Q, where Q = (I- W)-1 SAR has linear class boundaries in transformed space (QX, y) MRF-BC can represent non-linear class boundaries Experimental results MRF-BC can provide better classification accuracies than SAR But solution procedure is very slow Details in Recent paper in IEEE Transactions on Multimedia