Association rule mining based fuzzy manta ray foraging optimization algorithm for frequent itemset generation from social media

Abstract

Nowadays, the concept of data mining is employed widely and created a great deal of attention due to its fast arrival. Numerous approaches to frequent itemsets and association rule mining (ARM) are exemplified in recent years, but still, the performances based on scalability and processing time are considered as a major drawback that results in obtaining the solutions with very poor quality. To overcome such shortcomings, this article proposes three significant phases, namely, the data pre-processing phase, data pre-processing, frequent itemset mining, and ARM. In data pre-processing phase, the collected twitter datasets are pre-processed to eliminate redundant data and convert them into an appropriate format for further mining. In the frequent itemset mining phase, an Apriori algorithm is employed for the exact mining of frequent itemsets. The ARM phase utilizes the fuzzy manta ray foraging (FMRF) optimization algorithm that involves the generation of association rules from the huge itemsets thereby achieving minimum confidence and minimum support value. Here, the recent tweets regarding Covid-19, trump2020, joebiden, draintheswamp, and Godzilla are the datasets collected from the Twitter web link. The experimental analysis and the comparative performances are performed for various simulation measures and the results reveal that the proposed approach provides effective performances when compared with various other existing approaches.

Preserving Privacy in Association Rule Mining Using Metaheuristic-Based Algorithms: A Systematic Literature Review

“…MRFO has three unique foraging mechanisms: chain foraging, cyclone foraging, and somersault foraging, each of which serves as an aspect of the search process. Lakshmi and Krishnamurthy [81] adopted MRFO to develop a Fuzzy Manta Ray Foraging (FMRF) optimization algorithm to generate association rules from large itemsets. In the FMRF approach, the chain foraging phase retrieves recurrent itemsets from transactional databases.…”

Innovations in frequent itemset mining: challenges and opportunities

“…These patterns are essential for making data-driven decisions, identifying trends, and gaining deeper insights into complex datasets [13,14]. Consequently, researchers and practitioners have harnessed this technique to extract valuable information and drive data-driven processes in numerous industries and applications [15,16].…”

Manta Ray Foraging Optimization Algorithm: Modifications and Applications

“…Lakshmi and Krishnamurthy [85] proposed an association rule mining-based fuzzy MRFO approach for frequent item set generation from social media, where MRFO adjusts the FLS parameters. In the study, the association rule mining stage exploits the optimized fuzzy-based MRFO algorithm, generating association rules from massive item sets, attaining the least confidence and support value.…”