Evaluating Association Rules in Boolean Matrix Factorization

Association rules, or association rule mining, is a well-established and popular method of data mining and machine learning successfully applied in many different areas since mid-nineties. Association rules form a ground of the Asso algorithm for discovery of the first (presumably most important) factors in Boolean matrix factorization. In Asso, the confidence parameter of association rules heavily influences the quality of factorization. However, association rules, in a more general form, appear already in GUHA, a knowledge discovery method developed since mid-sixties. In the paper, we evaluate the use of various (other) types of association rules from GUHA in Asso and, from the other side, a possible utilization of (particular) association rules in other Boolean matrix factorization algorithms not based on the rules. We compare the quality of factorization produced by the modified algorithms with those produced by the original algorithms.