Fuzzy data mining in higher dimensions for data analysis

To extract fuzzy rules from databases or data files, we first select a set of attributes to associate and restrict all records (rows) to these. We next embed these feature vectors of small dimension into a high dimensional feature space by a Gaussian kernel mapping that yields a symmetric fuzzy membership matrix. The entry value at row i and column j is a fuzzy truth value that feature vectors i and j are in the same cluster. We look for clusters where features A and B associate in some way, e.g., (A is HIGH) and (B is LOW), so if the support and confidence are high enough, we accept that rule. The cluster centers become centers of fuzzy set membership functions to use in fuzzy modus ponens with the fuzzy rules. We apply our novel algorithm to analyze two difficult well known datasets.