1. Presented by Matt Brunner
Evaluating Trends in Sasquatch Sightings in North America Using Neural Networks
Presented by Matt Brunner

2. Adapt Data from BFRO.net into Useful Format

3. Adapt Data from BFRO.net into Useful Format

4. Narrow Focus to the Lower 48

5. Evaluation Using a Multilayer Perceptron Network
Developed using the neural network toolbox, 10 hidden nodes.
Goal is to correctly classify states within their summary quantile.
Poorly suited to this data set.

6. Evaluation Using K-Means Clustering
Using coordinates from population center information from U.S. Census Bureau
Each data point has four dimensions:
Latitude
Longitude
Year
Number of Reports

7. Evaluation Using K-Means Clustering
K =10 Clusters K = 25 Clusters

8. Evaluation Using K-Means Clustering
2D representation of 4D data.
Effect of more densely populated areas evident.
Higher K values likely to align a cluster center and a population center
K =50 Clusters

9. Evaluation Using K-Means Clustering

10. Conclusion
K-means is a much better method for analyzing this data set compared to an MLP.
Difficult to account for all variables and be able to visualize.
Developing a 2D Self-Organizing Map to further explore this data.

11. Works Cited
Bigfoot Field Researchers Organization.
United States Census Bureau, Centers of Populations By State: _Mean_ST.txt