25-10-2012, 05:21 PM
Method for Classifying a Random Process for Data Sets in Arbitrary Dimensions
ABSTRACT
Amethod is provided for automatically characterizing data sets containing data points described by d-dimensional vectors
obtained by measurements, such as with sonar arrays, as either random or non-random. The data points are located by the
d-dimensional vectors in a d-dimensional Euclidean space which may comprise any number d of dimensions and may
comprise more than three dimensions. Large or small sets of data may be analyzed. A virtual volume is determined which
contains data points from the maximum and minimums of the d-dimensional vectors. The virtual volume is then partitioned.
The probability of each partition containing at least one data point for a random distribution is compared to a measurement
of the number of partitions actually containing at least one data point whereby the data set is characterized as either random
or non-random.