Two-stage Attribute Weighting IB Algorithm for Non Co-occurrence Data
Non co-occurrence data does not appear in the form of co-occurrence of two variables X,Y,but rather as a sample of values of an unknown function Z(X,Y).The co-occurrence transformation of non co-occurrence data is necessary for clustering on the concept of Shannon entropy.However,these clustering algorithms treat all features fairly and set weights of all features equally.Therefore,the paper proposes a two-stage attribute weighting IB Algorithm(TPAW-sIB).At two stages of the co-occurrence transformation,we highlight representative features and dim irrelevant features from three viewpoints:non co-occurrence,co-occurrence and both.Experiments show that the TPAW-sIB algorithm is superior to the ROCK algorithm,the COOLCAT algorithm and the LIMBO algorithm.