The concept based on data mining has drawn considerable attention from various database professionals and research scholars. The progression of computer-based advancements, namely database management and data storage has facilitated the storage of large data and the data mining approaches are employed to gain valuable information from huge databases. Recently, several techniques to association rule mining (ARM) and frequent itemset mining (FIM) have been established; yet the efficiency based on execution time and scalability continues to be seen as a significant limitation that results in poor solution quality. Therefore, it is necessary to enhance the consistency that signifies the total number of frequently discovered frequent itemsets. This paper proposes three different phases namely the pre-processing phase, FIM phase and ARM phase. In the first pre-processing phase, the Twitter databases are pre-processed and converted into a suitable format for FIM. Here, the tweets are converted into related feature sets and items. In the second FIM phase, an improved Apriori algorithm is 1utilized in mining and extracting the frequent Then in the final phase, an adaptive billiard inspired optimization (ABIO) algorithm which is the integration of neural network (NN) optimization algorithm and billiard inspired optimization (BIO) algorithm is proposed for the optimal generation of association rules with minimum support and confidence from the huge itemsets. Finally, the recent tweets based on covidvaccine, BTSlivestreaming, KFC, McDonald’s as well as lockdown achieved using the hashtag is evaluated for various performance measures, like precision, recall, [Formula: see text]-measure, execution time and memory utilization. Also, comparative analyses are performed to evaluate the efficiency of the proposed technique.
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.
The data are generated by the sources are very large in number with variety of form. These data are organized in to specific format in order to handle properly. Data mining methods are addressed various problem during data extraction process to analytical process. The relevant data are extracted by applying pattern over the huge databases. Association rule mining introduces the method to extracts the related data from the datasets using the performance metrics like support and confidence. Traditional algorithm uses this metrics which is restricted to common attribute format. This problem is addressed by using generic attribute format with frequent pattern mining. The main objective of the paper is to analyze the algorithm and performance metrics related to the frequent patter mining or relevant data. Association rule mining has analyzed with various parameters in single connectivity and multi connectivity rules. Social networking suffers various problem because of uncertain data arrived for processing which is analyzed with various efficiency related elements. The analysis and prediction are also compared with the machine algorithms like classification and clustering and so on. Various frequent pattern mining algorithm is analyzed and review has been carried out based on the performance level.
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