Computational method for discovering patterns in data sets

A method in a computer of pattern discovery in a data base, comprising the steps of:

• a) operating a computer to provide a data base comprising M samples, each sample being described in terms of N attributes represented by X={X.sub.1, . . . Z.sub.N }, each attribute X.sub.i comprising a random variable taking on values from its alphabet .alpha..sub.i ={.alpha..sup.i, . . . , .alpha..sup.min} where m.sub.i is the cardinality of an alphabet of the ith attribute, wherein a primary event is a realization (x.sub.i) of X.sub.i which takes on a value from .alpha..sub.i ;
• b) operating said computer to set an initial pattern candidate set C comprising a plurality of candidates c and an Attributed Hypergraph (AHG) pattern set P (AHG P) to be empty, placing all primary events r.sub.i into a primary event set R, and setting order O=1;
• c) operating said computer to increment O and generating a candidate set C of order O from previous and primary event set R and if C is empty, go to step (e);
• otherwise go to step (d);
• (d) operating said computer to count, for each candidate c in C, its occurrences o and to calculate its expected occurrence e given by
• ›e=M o.sub.i /M! ##EQU21## where o.sub.i is the occurrence of the i-th primary event in c and M is the total number of instances in said database, if said expected occurrence is less than a preselected threshold, remove c from C, if the expected occurrence is greater than said preselected threshold, calculate an adjusted residual d of this candidate given by
• ›d=(o-e)/.sqroot.V! ##EQU22## where v is a maximum likelihood estimate of the variance of residual (o-e), if .vertline.d.vertline. is larger than a preselected confidence level, put c into AHG P, and if c is a negative pattern and the occurrence o is less than said preselected threshold, remove c from C, and if order O is equal to a highest order in demand, go to step (e) below, otherwise go to step (b);
• (e) operating said computer to output AHG P.