Patent ID: 6460035
Filing Date: 2002-10-01
Classification: G06F,G06K,Y10S

Abstract:
A data mining system adapted to generate a cluster model from a data set comprising a plurality of objects, each object including a plurality of attributes, said attributes including a set of discrete ordinal attributes, said system including an iterative cluster definition means, the or each cluster having a distribution attribute associated with each of said set of discrete ordinal attributes, said cluster definition means including:means for determining, for each cluster, a conditional probability density (pj(x,z,q)) of an object lying in a cluster; means for determining, for each cluster and for each object, a posterior probability (hij) of an object lying in a cluster, said posterior probability being a function of said conditional probability density of the cluster (pj(x,z,q)), a mixing fraction for said cluster (&agr;j) and an unconditional probability density (p(x,z,q)); and means for determining, for each object attribute and for each cluster, a next cluster distribution attribute (&mgr;jk,Vjk; &ngr;jk,Wjk; &pgr;jk,cjk), said distribution attribute being a function of said posterior probability, said object attribute value and a sum of said posterior probabilities; wherein said means for determining the conditional probability density of an object lying in a cluster is characterised by means for determining the conditional probability density of an object having a discrete ordinal attribute value within a finite range of attribute values lying in a cluster, said conditional probability density for said discrete ordinal attribute being a function of an integral of a conditional probability function across a sub-range of said discrete ordinal attribute range of values, said sub-range comprising an upper bound and a lower bound bounding said discrete ordinal attribute value.