Nowadays, the information technology has been growing rapidly with the evolution of larger databases and datasets. For viewing or retrieving information from larger databases is seems to be a challenging task. The discovery of functional dependencies in a dataset is of great importance for database redesign, anomaly detection and data cleansing applications. Objective: The main objective of this paper is to fetch the common similar functional details from the database and splits them separately and provide different set of instructions for those separated information. Finding: In this paper, a new algorithm is designed called SFD (Similarity Functional Dependancy) algorithm for discovering all functional dependencies in a dataset. SFD follows a depth first traverse of the attribute lattice that combines aggressive pruning and efficient result verification. Novelty: This paper describes the concept about the fetching of data from the database in which there is repeated information or same details i.e. database with similar functional values. Improvement: This concept has been implemented with help of the similarity functional dependencies algorithm, which helps us to find out the repeated or similar database values or information. The new approach is able to scale far beyond the already existing algorithm known as functional dependency.

Keywords: Data Mining, Hierarchical algorithm, SFD Algorithm, Anomaly Detection, Prediction.