Abstract

The basic idea of traditional density estimation is to model the overall point density analytically as the sum of influence functions of the data points. However, traditional density estimation techniques only consider the location of a point. Supervised density estimation techniques, on the other hand, additionally consider a variable of interest that is associated with a point. Density in supervised density estimation is measured as the product of an influence function with the variable of interest. Based on this novel idea, a supervised density-based clustering named SCDE is introduced and discussed in detail. The SCDE algorithm forms clusters by associating data points with supervised density attractors which represent maxima and minima of a supervised density function. Results of experiments are presented that evaluate SCDE for hot spot discovery and co-location discovery in spatial datasets. Moreover, the benefits of the presented approach for generating thematic maps are briefly discussed.

Citations