Estimating the novelty of points in high-dimensional spaces requires balancing local and global characteristics of the data. Our approach addresses this challenge with a density-based novelty metric that reflects the underlying structure of the data. We employ MI analysis to determine both the extent of local neighborhoods and the degree of influence between points within these neighborhoods.