Geospatial data can come in several forms, for example locations of specific points, or polygons that define areas of interest. When both point and polygon data are present, it is often useful to identify the enclosing polygon for each point, so information from both tables can be merged
This data function clusters objects together based on similarities between the objects in each cluster. After identifying clusters, the function then ranks the input variables according to their influence on cluster formation.
Gradient boosting is an ensemble-decision-tree, machine learning data function that’s useful to identify variables that best predict some outcome and build highly accurate predictive models. For example, a retailer might use a gradient boosting algorithm to determine the propensity of customers to buy a product based on their buying histories.
Two-dimensional binning with hexagonally-arranged bins of (x,y) inputs. Useful in Spotfire for simplifying an (x,y) scatter plot with a large number of points to produce a sampling density plot. Also if an optional 3rd value column is included, a Spotfire scatter plot visualization can be constructed for the mean value across cells.