CD-sIB:A Kind of sIB Algorithm Orient to Categorical Data

The sIB algorithm has previously been only applied to the analysis of co-occurence data.Therefore,it cannot directly analyze categorical data that do not appear in the form of co-occurrence of two variables X,Y.Aiming to solve the problem,this paper proposes a CD-sIB algorithm for automatically analyzing categorical data based on the theory of sIB algorithm.According to the nature that categorical data is discrete and its distinct attribute value is finite,CD-sIB algorithm counts joint distribution of relevant variable X,Y based on the occurence frequency of attribute value by extending the attributes of dataset and utilizing binarization to process the categorical data.Consequently,our algorithm can be effectively employed in analyzing the categorical data.As shown by our experimental results,CD-sIB outperforms the GAClust and the K-modes algorithm,and it achieves high precision and efficiency in analyzing categorical data,especially in the analysis of categorical data which is highly generalizable and comparatively balanced in the data distribution of each class.