Association rule mining using fuzzy logic and whale optimization algorithm

Sharmila, S.; Vijayarani, S.

doi:10.1007/s00500-020-05229-4

Cited by 32 publications

(11 citation statements)

References 15 publications

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“…This section provides the comparative analysis regarding the implementation time and memory utilization for the proposed FMRF, fuzzy rules using whale optimization algorithm (FRUWOA), 37 particle swarm optimization algorithm (PSO), 54 whale optimization algorithm (WOA), 55 as well as MRF optimization algorithm. From the analysis, it is clear that the proposed approach provides very low implementation time when compared with various other approaches.…”

Section: Comparative Resultsmentioning

confidence: 99%

“…The ARM using fuzzy logic and whale optimization algorithm was proposed by Sharmila et al 37 The performance metrics employed for evaluating the proposed approach were Crisp value, degree of membership function, execution time, and memory space. The experimental analysis has proven that the performances of this approach were high with less execution time.…”

Section: Review Of Related Workmentioning

confidence: 99%

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

Lakshmi¹,

Krishnamurthy²

2021

Concurrency and Computation

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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.

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Section: Comparative Resultsmentioning

confidence: 99%

Section: Review Of Related Workmentioning

confidence: 99%

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

Lakshmi¹,

Krishnamurthy²

2021

Concurrency and Computation

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“…In image classification and diagnosis, computer-aided diagnosis technology has become a hot topic. CAD assisted in time can provide a lot of help for doctors to extract image feature information, can effectively reduce the workload of doctors, and also has important value in the early development of diseases [ 12 – 14 ]. In the process of massive data processing, it is generally considered that the model is complex enough to extract more information.…”

Section: Introductionmentioning

confidence: 99%

Online Diagnosis and Classification of CT Images Collected by Internet of Things Using Deep Learning

2022

Computational and Mathematical Methods in Medicine

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Deep learning technology has recently played an important role in image, language processing, and feature extraction. In the past disease diagnosis, most medical staff fixed the images together for observation and then combined with their own work experience to judge. The diagnosis results are subjective, time-consuming, and inefficient. In order to improve the efficiency of diagnosis, this paper applies the deep learning algorithm to the online diagnosis and classification of CT images. Based on this, in this paper, the deep learning algorithm is applied to CT image online diagnosis and classification. Based on a brief analysis of the current situation of CT image classification, this paper proposes to use the Internet of things technology to collect CT image information and establishes the Internet of things to collect the CT image model. In view of image classification and diagnosis, the convolution neural network algorithm in the deep learning algorithm is proposed to diagnose and classify CT images, and several factors affecting the accuracy of classification are proposed, including the convolution number and network layer number. Using the CT image of the hospital brain for simulation analysis, the simulation results confirm the effectiveness of the deep learning algorithm. With the increase of convolution and network layer and the decrease of compensation, the accuracy of image classification will decline. Using the maximum pool method, reducing the step size can improve the classification effect. Using relu function as the activation function can improve the classification accuracy. In the process of large data set processing, appropriately adding a network layer can improve classification accuracy. In the diagnosis and analysis of brain CT images, the overall classification accuracy is close to 70%, and in the diagnosis of tumor diseases, the accuracy is higher, up to 80%.

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“…Yin (2020) et al proposed an improved WOA based on the chaos theory and logistic mapping technique for brain tumor classification. As for ARM, Sharmila and Vijayarani (2021) used fuzzy logic and WOA for frequent item identification and association rule generation. Heraguemi et al (2021) proposed an updated WOA for mining association rules (WO-ARM), improving running time and memory usage.…”

Section: Introductionmentioning

confidence: 99%

Association Rule Mining Based on Hybrid Whale Optimization Algorithm

Cai

Wang

et al. 2022

International Journal of Data Warehousing and Mining

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Association Rule Mining(ARM) is one of the most significant and active research areas in data mining. Recently, Whale Optimization Algorithm (WOA) has been successfully applied in the field of data mining, however, it easily falls into the local optimum. Therefore, an improved WOA based adaptive parameter strategy and Levy Flight mechanism (LWOA) is applied to mine association rules. Meanwhile, a hybrid strategy that blends two algorithms to balance the exploration and exploitation phases is put forward, that is, grey wolf optimization algorithm (GWO), artificial bee colony algorithm (ABC) and cuckoo search algorithm (CS) are devoted to improving the convergence of LWOA. The approach performs a global search and finds the association rules sets by modeling the rule mining task as a multi-objective problem that simultaneously meets support, confidence, lift, and certain factor, which is examined on multiple data sets. Experimental results verify that the proposed method has better mining performance compared to other algorithms involved in the paper.

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Association rule mining using fuzzy logic and whale optimization algorithm

Cited by 32 publications

References 15 publications

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

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

Online Diagnosis and Classification of CT Images Collected by Internet of Things Using Deep Learning

Association Rule Mining Based on Hybrid Whale Optimization Algorithm

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