Patent ID: 7542954
Filing Date: 2009-06-02
Classification: G06K,G06N

Abstract:
1. A method executed on a computer for representing a dataset for data classification, the method comprising: clustering the dataset using an unsupervised, non-parametric clustering method to generate a set of clusters each comprising a set of data points in an image of the dataset; clustering the data points of each cluster of the set of clusters using a supervised, partitional clustering method to partition each cluster into a specified number of sub-clusters each comprising a subset of the set of data points of the cluster; generating a density estimate value of each grid point of a set of grid points sampled from the image at a specified resolution for each sub-cluster in the image using a kernel density function; evaluating the density estimate value of each grid point for each sub-cluster to identify a maximum density estimate value for the grid point and a sub-cluster associated with the maximum density estimate value for the grid point; adding each grid point for which the maximum density estimate value exceeds a specified threshold to the sub-cluster associated with the maximum density estimate value for the grid point; and for each cluster of the set of the clusters, merging the sub-clusters of the cluster into a cluster region in the image corresponding to the cluster to form a shape interpolated representation of the set of clusters.