ABSTRACT:

Data mining or knowledge discovery in databases in easy words is that the non-trivial extraction of implicit, previously unknown and potentially helpful info from data. It deals with the invention of hidden knowledge, surprising patterns and new rules from giant databases. Knowledge discovery in databases is the method of identifying a legitimate, potentially helpful and ultimately understandable structure in knowledge. Datasets of hepatitis are collected from the benchmark repository and training datasets are revealed. Information mining tasks as well as classification, clustering, regression etc., In order to get the classification rules, ant miner algorithm is used. The ant miner algorithm is based on the behavior of ants in looking out of food. The proposed technique extracts the classified rules using fuzzy based ant miner algorithm (FACO). The training set is taken and also the FACO algorithm is applied initially for classifying the explicit attributes. Using heuristic functions, the most effective rules are generated. Next, rule pruning is performed to get the optimized rules based mostly on quality functions. The accuracy of the designed system is decided using the check cases. FACO is used to bring out with better quality for the classified rules. The project aims at getting the simplest rules with most accuracy. It provides the secondary opinion for the doctors and it predicts the hepatitis in the earlier stage.