Patent ID: 7318051

Claim:
A computer-implemented method for identifying a pattern within a large dataset, wherein data points in the dataset correspond to a physical measurement and have a plurality of features that describe attributes of the data point, the method comprising: inputting the large dataset into a computer system having a processor and a memory; selecting a feature subset of the large number of features for processing in a learning machine by executing a feature selection algorithm on the dataset to identify the subset of features, wherein the algorithm is selected from the group consisting of l 0 -norm minimization and unbalanced correlation, wherein l 0 -norm minimization comprises finding a smallest number of non-zero elements of a weight vector w in the relationship D(x)=w·x+b, and unbalanced correlation comprises dividing the dataset into unbalanced groups of positive and negative examples and ranking features according to a success criterion for correctly classifying the dataset; processing the feature subset of the data points of the large dataset to identify the pattern; and generating an output to a printer or display device, the output comprising the identified pattern within the large dataset for the feature subset.