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Introduction

The data of Indiana Census 2000 includes 200,000 records and each has 55 dimensions or fields.  By gradually decreasing temperature (distance scale), DAC discovers more clusters (partition of subgroups) and avoid from poor results of local minima. The demo shows how one could use clustering to find locations of different population types (e.g. age and ethnics) and display the results in Microsoft virtual Earth.

 
General Formula DAC GM GTM DAGTM DAGM Decrease temperature (distance scale) to discover more clusters based on ethnics

 

Changing Resolution of GIS Clustering

 

Decrease temperature (distance scale) to discover more clusters based on population

 

 

Runtime System Used

  • micro-parallelism
    • Microsoft CCR (Concurrency and Coordination Runtime)
      • supports both MPI rendezvous and dynamic (spawned) threading style of parallelism
      • has fewer primitives than MPI but can implement MPI collectives with low latency threads
      • http://msdn.microsoft.com/robotics/
    • MPI.Net
  • macro-paralelism (inter-service communication)
    • Microsoft DSS (Decentralized System Services) built in terms of CCR for service model
    • Mash up
    • Workflow (Grid)