Finding Anomaly With Fuzzy C-means ANN Using Semi-Supervised Approach

The FC-ANN (Artificial Neural Network) is used to speed up the technique. The anomaly Outlier detection is primary in various data-mining applications. Outlier detection methods have been suggested for number of application such as, fraud detection, voting irregularity analysis, data cleansing, clinical trials, network intrusion, severe weather prediction, geographic information system, credit cards, athlete performance analysis and other data mining tasks proposed algorithm. This proposed system attaches the rough set theory, fuzzy set theory and semi-supervised learning to detect outliers as well as is a new try in area of outlier detection for semi-supervised learning. Without considering those points located in lower approximation of a cluster, proposed algorithm need to discuss the possibility of the points in boundary to be assigned as outliers and has number of advantages over semisupervised outlier detection. In this proposed algorithm will be applied to various outlier detection fields which has only partially labeled samples, especially that does not make a certain judgment in uncertain conditions. The proposed system proposes the technique FC-ANN that may add parameters to speed up the technique.