Fuzzy Based Ant Miner Algorithm in Datamining for Hepatitis

Data mining or knowledge discovery in databases in simple words is the non-trivial extraction of implicit, previously unknown and potentially useful information from data. It deals with the discovery of hidden knowledge, unexpected patterns and new rules from large databases. Knowledge discovery in databases is the process of identifying a valid, potentially useful and ultimately understandable structure in data. Datasets of hepatitis are collected from the benchmark repository and training datasets are revealed. Data mining tasks including classification, clustering, regression etc., In order to discover the classification rules, ant miner algorithm is used. The ant miner algorithm is based on the behavior of ants in searching of food. The proposed method extracts the classified rules using fuzzy based ant miner algorithm (FACO). The training set is taken and the FACO algorithm is applied initially for classifying the categorical attributes. Using heuristic functions, the best rules are generated. Next, rule pruning is performed to obtain the optimized rules based on quality functions. The accuracy of the designed system is determined using the test cases. FACO is used to bring out with better quality for the classified rules. The project aims at obtaining the best rules with maximum accuracy. It provides the secondary opinion for the doctors and it predicts the hepatitis in the earlier stage.