A New Framework for Optimal Hesitation Pattern Mining

Abstract

Abstract

In mathematical optimization, the firefly algorithm is a metaheuristic approach. It has been proposed by Xin-She Yang and inspired by the flashing behavior of fireflies. Proposed research used Firefly Algorithm for discovering the best Association rules. In this species, it is always the female who glows, and only the male has wings. In other species, Luciola lusitanica, both male and female firefly may emit light and both have wings. If a firefly is hungry or looks for a mate its light glows brighter in order to make the attraction of insects or mates more effective. Since the stored data are not always exact and precise, some means are required to handle this aspect of data and extract the useful information, (e.g., hesitation information) arising from such uncertainties. For this purpose vague set theory using Firefly Algorithm emerge as powerful tool will be applied to optimize the Association rule for efficiently modeling the uncertainties that occur in datasets. A new efficient approach is proposed for exploring high-quality association rules. The proposed approach is based on firefly algorithm, which is an optimization algorithm used in extracting Vague or Hesitant Information. In this approach, we initialize the population of firefly. Calculate the fitness of each firefly. If fitness of firefly (j) is greater than firefly (i) than firefly (i) move toward firefly (j). Calculate the intensity of firefly (i) and update light intensity of firefly (i). This iteration continues till maximum or desired condition occurs.

Keywords: Hesitant Mining, Optimization technique, Association rule, Vague association rule, firefly algorithm

Cite this Article

Aditi Jain, Akhilesh Tiwari. A New Framework for Optimal Hesitation Pattern Mining. Journal of Communication Engineering & Systems. 2017; 7(2): 34–41p.