A PRACTICAL APPLICATION OF SIMULATED ANNEALING TO CLUSTERING

We formalize clustering as a partitioning problem with a user-defined internal clustering criterion and present SINICC, an unbiased, empirical method for comparing internal clustering criteria. An application to multi-sensor fusion is described, where the data set is composed of inexact sensor "reports" pertaining to "objects" in an environment. Given these reports, the objective is to produce a representation of the environment, where each entity in the representation is the result of "fusing" sensor reports. Before one can perform fusion, however, the reports must be "associated" into homogeneous clusters. Simulated annealing is used to find a near-optimal partitioning with respect to each of several clustering criteria for a variety of simulated data sets. This method can then be used to determine the "best" clustering criterion for the multi-sensor fusion problem with a given fusion operator.