초록 ▼

A method and apparatus are provided for automatically characterizing the spatial arrangement among the data points of a time series distribution in a data processing system wherein the classification of said time series distribution is required. The method and apparatus utilize a grid in Cartesian c

A method and apparatus are provided for automatically characterizing the spatial arrangement among the data points of a time series distribution in a data processing system wherein the classification of said time series distribution is required. The method and apparatus utilize a grid in Cartesian coordinates to determine (1) the number of cells in the grid containing at least-one input data point of the time series distribution; (2) the expected number of cells which would contain at least one data point in a random distribution in said grid; and (3) an upper and lower probability of false alarm above and below said expected value utilizing a discrete binomial probability relationship in order to analyze the randomness characteristic of the input time series distribution. A labeling device also is provided to label the time series distribution as either random or nonrandom, and/or random or nonrandom.

대표청구항 ▼

1. A method for characterizing a spatial arrangement among a pre-selected number N of data points of a time series distribution of pre-selected duration in a display/operating system wherein a classification of the spatial arrangement of said time series distribution is required, said method compris

1. A method for characterizing a spatial arrangement among a pre-selected number N of data points of a time series distribution of pre-selected duration in a display/operating system wherein a classification of the spatial arrangement of said time series distribution is required, said method comprising the steps of: inputting said time series distribution of no more than N data points and no longer than said pre-selected time interval into said display/operating system; determining the difference in value .DELTA.Y between the data point in said time series distribution having the greatest value and the data point in said time series distribution having the smallest value; creating a virtual window having an area containing said time series distribution of data points, said area being equal to N*.DELTA.Y; subdividing substantially the entirety of said area of said window into a plurality k of cells, each said cell having the same polygonal shape and defining the same area value; determining a number m of said cells containing at least one of said data points of said time series distribution; determining an expected number M of cells containing at least one of said data points in said time series distribution in the event that said time series distribution is random in structure; and characterizing said input time series distribution as clustered in the event that m is less than M, random when m is equal to M, and uniform when m is greater than M.