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Automatic clustering method

  • US 5,329,596 A
  • Filed: 09/11/1992
  • Issued: 07/12/1994
  • Est. Priority Date: 09/11/1991
  • Status: Expired due to Term
First Claim
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1. An automatic pattern recognition method to determine a category of input data points of unknown category, comprising:

  • performing a first learning step of dividing sample data points into classes, which includesgenerating sample data points whose categories are known,generating a plurality of standard patterns in an n-dimensional space at arbitrary positions near a center of sample data points distribution, which standard patterns correspond to an arbitrary plurality of classes of the sample data points,calculating the distances between individual sample data points and each of the standard patterns to determine the nearest standard pattern for each sample data point,temporarily classifying each sample data point as belonging to the class corresponding to the nearest standard pattern,calculating the summation of differences between each standard pattern and the corresponding sample data points for each dimension for each class,moving the standard patterns in a direction represented by the summation of differences for each class,temporarily classifying each sample data point as belonging to the class corresponding to the nearest moved standard pattern,recalculating the distances between each sample data point and each moved standard pattern to determine the nearest standard pattern for each sample data point,moving the standard patterns in a direction represented by the summation of differences,repeating the preceding three steps until the summation of differences between each standard pattern and the corresponding sample data points is smaller in each dimension than a set specific value, anddetermining a final position of each standard pattern and sample data points belonging to the class represented by the corresponding standard pattern;

    performing a second learning step, when the sample data points belonging to one class do not belong to the same category, which includesdividing the one class into a plurality of subclasses, andrepeating said step of dividing for the sample data points for each remaining class and subclass that has sample data points of more than one category;

    performing a third learning step of relating the standard patterns, classes and subclasses obtained in the first learning step and second learning step to each other in a tree-structure representation and storing the tree-structure representation in memory;

    inputting data points of unknown category; and

    determining recognition/nonrecognition of the input data points of unknown category based on correspondence/lack of correspondence between the input data points and the stored tree-structure representation.

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