naïve bayes evidence accumulation k-modes clustering: a new method for classifying binary data and its application on real data of injecting drug users

Background & aim: clustering is the method of classifying discrete data such as k- modes, and naïve bayes classifier is the classification to predict the unknown real classes. in this research, we improve the k-modes results by applying the evidence accumulation (ea) method to keep the initial mode vector to use in the naïve bayes ea k-mode. methods & materials: the methods are applied to four real datasets, which the true classes are specified, for checking the external validity and purity of our methods. the free programming software r with package klar for k-modes, ea, and package e1071 for naïve bayes is used. in addition, the methods are applied to the data of injecting drug users (idu) national dataset with sample size 2546. results: the ea k-modes algorithm applied to five real datasets then with the kept initial mode vector, rerun the k-modes. the results indicate the purity in the ea k-modes (0.544, 0.862, 0.914, 0.944, 0.625) has significant different with classic k-modes (0.497, 0.610, 0.404, 0.650, 0.625). finally, we applied the naïve bayes classifier with prior probability finds in ea k-modes. for k=2 naïve bayes ea k-modes made better clustering (0.71, 0.873 against 0.625, 0.862 ea k-mode and 0.497, 0.61 k-mode). conclusion: in this paper, we proposed naïve bayes ea k-modes as a new method for clustering of binary data. our new method leads to stable clustering compare with the previous studies. the naïve bayes ea k-modes method improves the purity and establishes a better separation.