Mining Frequent Item sets on Large Scale Temporal Data

Haseena, S.; Manoruthra, S.; Hemalatha, P.; Akshaya, V.

doi:10.1109/iceca.2018.8474890

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Table 3 shows the research findings without preprocessing and with preprocessing technique like noise removal by removing missing values, normalization using min-max, normalization using Z-score, and combined results. Various metaheuristic algorithms are applied for various applications [34][35][36][37][38][39]. Table 4 displays the results of experimentation using several feature selection strategies such as the genetic algorithm, particle swarm optimization, and Moth-Flame Optimization.…”

Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

Moth-Flame Optimization for Early Prediction of Heart Diseases

Haseena¹,

Priya²,

Subbaraj³

et al. 2022

Computational and Mathematical Methods in Medicine

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Heart disease is among the leading causes of mortality globally. Predicting cardiovascular disease is a major difficulty in clinical data analysis. AI has been demonstrated to be powerful in deciding and anticipating an enormous measure of information created by the health domain. We provide a unique method for finding essential traits employing machine learning approaches in this paper, which enhances the effectiveness of identifying heart diseases. Decision tree (DT), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN) are the classification techniques used to create the proposed system. Ensemble stacking integrates the four classification models to create a single best-fit predictive model using logistic regression. Many explorations have been directed at the identification of cardiac infection; however, the exactness of the outcomes is poor. Accordingly, to further enhance the efficiency, Moth-Flame Optimization (MFO) algorithm is proposed. The feature selection strategies are used to improve the classification accuracy while shortening the execution time of the classification system. Medical data are used to assess the probability of heart disease based on BP, age, gender, chest ache, cholesterol, blood sugar, and other variables. Results revealed that the proposed system excelled other existing models, obtaining 99% accuracy in the Cleveland dataset.

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Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

Moth-Flame Optimization for Early Prediction of Heart Diseases

Haseena¹,

Priya²,

Subbaraj³

et al. 2022

Computational and Mathematical Methods in Medicine

Self Cite

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“…In 2018, according to S.Haseena et al [7] frequent pattern mining has developed into an essential data mining method that is primarily concentrated in a variety of research disciplines. The patterns that occur frequently throughout the dataset are referred to as "frequent patterns."…”

Section: Literature Surveymentioning

confidence: 99%

MR-AT: Map Reduce based Apriori Technique for Sequential Pattern Mining using Big Data in Hadoop

Sujit R Wakchaure

2023

IJRITCC

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One of the most well-known and widely implemented data mining methods is Apriori algorithm which is responsible for mining frequent item sets. The effectiveness of the Apriori algorithm has been improved by a number of algorithms that have been introduced on both parallel and distributed platforms in recent years. They are distinct from one another on account of the method of load balancing, memory system, method of data degradation, and data layout that was utilised in their implementation. The majority of the issues that arise with distributed frameworks are associated with the operating costs of handling distributed systems and the absence of high-level parallel programming languages. In addition, when using grid computing, there is constantly a possibility that a node will fail, which will result in the task being re-executed multiple times. The MapReduce approach that was developed by Google can be used to solve these kinds of issues. MapReduce is a programming model that is applied to large-scale distributed processing of data on large clusters of commodity computers. It is effective, scalable, and easy to use. MapReduce is also utilised in cloud computing. This research paper presents an enhanced version of the Apriori algorithm, which is referred to as Improved Parallel and Distributed Apriori (IPDA). It is based on the scalable environment referred as Hadoop MapReduce, which was used to analyse Big Data. Through the generation of split-frequent data regionally and the early elimination of unusual data, the proposed work has its primary objective to reduce the enormous demands placed on available resources as well as the reduction of the overhead communication that occurs whenever frequent data are retrieved. The paper presents the results of tests, which demonstrate that the IPDA performs better than traditional apriori and parallel and distributed apriori in terms of the amount of time required, the number of rules created, and the various minimum support values.

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A metaheuristic harmony search optimization–based approach for hateful and offensive speech detection in social media

Saroja,

Haseena

2024

Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications

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Mining Frequent Item sets on Large Scale Temporal Data

Cited by 3 publications

References 8 publications

Moth-Flame Optimization for Early Prediction of Heart Diseases

Moth-Flame Optimization for Early Prediction of Heart Diseases

MR-AT: Map Reduce based Apriori Technique for Sequential Pattern Mining using Big Data in Hadoop

A metaheuristic harmony search optimization–based approach for hateful and offensive speech detection in social media

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