Analogous Examination of Various Machine Learning Algorithm Applied to Big Data

There is a real increasing in the generating of data from different sources. Data mining (DM) is a useful method to elicit valuable information. Association rule mining (ARM) can assist in finding patterns and trends in big data. Also fuzzy logic plays a main role as assistance technique in handling big data issues. This review paper produces recent literature on hybridization regarding the association rule mining or other DM methods such as classification and clustering and fuzzy logic techniques in big data. Whereas a hybrid model of association rule and fuzzy logic is suggested to get a valuable knowledge for big data applications at good accuracy and less time, with the aid of distributed framework for big data handling (Hadoop, Spark and MapReduce). Different techniques and algorithms were used in these works and evaluated according to accuracy, sensitivity, recall and run time with a various result as Specificity = 86%, Sensitivity = 80% and F-measure = 2.5, or achieving high accuracy and shorter runtime compared to other methods and 98.5accuraccy of fitness function in pruning redundant rules. At the end of the paper we present the most used and prominent techniques that assist in providing a useful and valuable knowledge in different domains from a huge, unstructured and even heterogeneous data. The paper will be beneficial to the researches who interesting in the field of mining big data.

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“…Hadoop Big Data has emerged as a potent tool to process and manage data as a result of the growth of big data. [7].…”

Section: Big Data Platformsmentioning

confidence: 99%

A Hybrid Integrated Model for Big Data Applications Based on Association Rules and Fuzzy Logic: A Review

raad

Hamad

2023

ijcsm

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Machine learning algorithms in Bigdata Analysis and its applications: A review

Khoshaba

Kareem

Awla

et al. 2022

2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)

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Model Hybrid for Sales Forecast for the Housing Market of São Paulo

Moro

Weise

Bornia

2020

Real Estate Management and Valuation

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This research proposes a combined model of time series for forecasting housing sales in the city of São Paulo. We used data referring to the time series of sales of residential units provided by SECOVI-SP. The Exponential Softening, Box-Jenkins and Artificial Neural Networks models are individually modelled, later these are combined through five forecast combination techniques.The techniques used are Arithmetic Mean, Geometric Mean, Harmonic Mean, Linear Regression and Principal Component Analysis. The measures of accuracy to measure the results obtained and to select the best model are the RMSE, MAPE and UTheil of forecast. The results showed that Linear Regression with an independent variable, being a combination of the SARIMA model (2,0,0)(2,0,0)12 and MLP/RNA (12,10,1), provided a satisfactory performance, with an RMSE of 368.74, MAPE of 19.2% and UTheil of 0.315.The combination of time series models allowed a significant increase in forecast performance. Finally, the model was validated, using it to predict housing sales. The results show that the model has a good fit, thus demonstrating that using a housing sales forecasting model helps industry professionals minimize error and make sales and launch decisions.

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Analogous Examination of Various Machine Learning Algorithm Applied to Big Data

Cited by 3 publications

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A Hybrid Integrated Model for Big Data Applications Based on Association Rules and Fuzzy Logic: A Review

A Hybrid Integrated Model for Big Data Applications Based on Association Rules and Fuzzy Logic: A Review

Machine learning algorithms in Bigdata Analysis and its applications: A review

Model Hybrid for Sales Forecast for the Housing Market of São Paulo

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