An Efficienct Rule Mining Model Using Safe Semi Supervised Fuzzy C Means and ANN Techniques

We are living in the era known as information era where numerous sectors especially health sectors are put to handle tremendous amount of information. To reduce this burden, an efficient data mining technology has been employed which is a successful evolving technique that has a big future for helping businesses and concentrates on the most valuable data in their data warehouses. Thus, this paper presents the review of Safe Semi Supervised Fuzzy C Means (S³FCM) clustering algorithm which is the key aspect of this work aids in achieving the goal. It has been utilized to cluster and classify the clear and relevant global data formats that many other clustering approaches unable to handle. By limiting the subsequent predictions obtained by unsupervised clustering, incorrectly labelled samples are thoroughly investigated. Meanwhile, the other labelled sample’s predictions are equivalent to the assigned labels. As a result, the labelled samples are safely examined using a combination of unsupervised clustering and Semi-Supervised Clustering (SSC). Therefore, it has been clear that S³FCM yields better result compared to other techniques. The Artificial Neural Network (ANN) based classification algorithm is used, which learns from the training dataset to construct a model. This model helps in the classification of new objects.