Efficient Implementation of Fp Growth Algorithm On Library Data

Abstract

Data mining techniques are used in the field of many studies for various purposes. Everyday organizations collect huge amount of data from several resource. So, in this research, library data is considered as most famous application to mine that data to provide interesting patterns or rules for the future perspective. Implementation on it to generate rules and patterns using Frequent Pattern (FP)-Growth algorithm is the major concern of this research study. This study is to provide more guidance to the Liberian and the relation of a Liberian and a borrower. Frequent itemsets are generated based on the chosen borrowed books and minimum support value. The extracted frequent itemsets help the Liberian to make   decisions which book is placed near at which book and determine the risk level of library data at an early stage. The proposed method can be applied to library dataset to predict the risk factors with risk level of the books based on chosen factors.