Obesity epidemic is a well-documented public health problem in the United States. The dramatic increase in childhood obesity raises concern about the health of these youth as they approach adulthood. Environmental conditions have been identified as intervening factors through their impact on physical activity and eating habits. MDS provides a visualization method, which can easily integrate with other clustering models (e.g. Pairwise clustering), for analysis of the structure of medical Geographic's data and relationship between obesity and environment.
Drawing on existing databases maintained by university research centers, health care systems, government agencies, and commercial data providers.
Pre-processing data and generating pairwise distance matrix from patient records and Geographics data.
Conducting multi-variate analyses to explore the relative contributions of individual factors, physical environmental factors, and social environmental factors to the risk of obesity in children.
Data development is often the primary bottleneck in launching data mining projects.
The complexity of several important data mining algorithms, including Deterministic Annealing Clustering (DAC), scale proportionally to the square of the number of data points and so need significant computer resources.