Automatic clustering method

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 includes
• generating 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, and
• determining 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 includes
• dividing the one class into a plurality of subclasses, and
• repeating 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.