Association Rule Mining using FP-Growth and An Innovative Artificial Neural Network Techniques

Abstract

Abstract: With the prodigious proliferation of ginormous-scale data depots, a need for incorporating the empirical techniques of Data Mining (DM) with the effectiveness of commensurate systems to intuitively manage cosmic volumes of data has now risen. To quell these obstructions of managing data efficiently, in this document we present a new algorithm based on ANN for DM activities, which overcomes the problems in the current available algorithms of mining apropos of their execution time and interestingness and to prove its efficiency the new algorithm will be compared to a popular mining algorithm: FP Growth. The following sections of the paper are sorted as – section I exhibits the introduction, section II exhibits the previously done related works, section III is exemplifies of the methodologies, section IV exhibits the experimental setup of the analogous work, section V inculcates the experimental result and finally section VI depicts the conclusion of the respective work. Keyword: Data Mining, Association Rule Mining, FP Growth algorithm, Artificial Neural Networks.