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Methods and apparatus for outlier detection for high dimensional data sets

  • US 7,395,250 B1
  • Filed: 10/11/2000
  • Issued: 07/01/2008
  • Est. Priority Date: 10/11/2000
  • Status: Expired due to Fees
First Claim
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1. A method of optimizing data mining in a computer, the data mining being performed by the computer to detect one or more outliers within a high dimensional data set stored on a data storage device coupled to the computer, the data set representing a population of persons and the one or more outliers representing one or more persons within the population of persons, the method comprising the steps of:

  • determining one or more subsets of dimensions and corresponding ranges in the data set which are sparse in density using an algorithm comprising at least one of the processes of solution recombination, selection and mutation over a population of multiple solutions; and

    determining one or more data points in the data set which contain these subsets of dimensions and corresponding ranges, the one or more data points being identified as the one or more outliers;

    wherein the sets of dimensions and corresponding ranges in which the data is sparse in density is quantified by a sparsity coefficient measure.

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