Discovery of Fuzzy and Composite Fuzzy Association Rules in Meteorological Data

Abstract

Fuzzy Association Rule Mining (FARM) extends traditional ARM by evaluating and pruning rules based on interestingness measures to identify relevant patterns for various applications. The focus of this paper is to explore the application of FARM techniques demonstrating its algorithmic implementation in a meteorological dataset. Three major algorithms known as fuzzy Apriori, FTDA (Fuzzy Transaction Data-Mining Algorithm) and CFARM Composite Fuzzy Association Rule Mining) are experimented and analyzed. The experiment uses a real meteorological dataset spanning twenty years consisting some important attributes of weather such as rainfall, temperature, relative humidity, wind speed and bright sunshine hours of the North Bank Plain Zone (NBPZ) of the Brahmaputra River in Assam, India. The collected dataset is pre-processed into a transaction dataset and converted into a fuzzy dataset using membership functions. The three FARM algorithms are subsequently employed to uncover associations among various attributes within the fuzzy meteorological dataset. This study analyzes experimental results from three algorithms, focusing on factors like rule generation, computation time, and memory consumption. While Fuzzy Apriori provides comprehensive rule generation, it comes at the cost of higher computation time and memory usage. FTDA and CFARM, on the other hand, offer more efficient and significant rule generation, making them more suitable for large-scale, complex data analysis. The findings of this paper can contribute to the development of resilient and efficient data mining frameworks, enhancing the decision-making process for stakeholders in the meteorological domain. Thus, the paper introduces a new method for analyzing meteorological data using Fuzzy Association Rule Mining (FARM) techniques.