The Efficient Classification in Multi Relation Database Using Crossmine

Multi-relation classifications can be widely used in many disciplines, such as financial decision making, medical research, and geographical applications, and information stored in multiple relations needs to be used in decision making. Crossmine is an efficient and scalable approach for multi-relation classification. Crossmine algorithm has three step, first is find-rules, the rule has been gotten from find a rule process than remove all positive tuples satisfying rule while there are more than ten percent positif tuple left. The second is find a rule, this step has input from the result of find best predicate process, that is the complex predicate with most foilgain. If foilgain value is more than mingain, the predicate is added with rule, and max rule length less than six. Third is find best predicate, in this step we find the best predicate with definition, if the foilgain value more than the max gain value, the predicate will be saved and the bigger gain value will replace the last gain value for next comparative process. In other side, the accuracy is computed from each rule that produce in find rules process. The test for this application use the sum tuple of 200, 500, 1000, 5000 for measuring the level of accuracy from rule which is produced by crossmine algorithm.