Rainfall estimation from MSG images using fuzzy association rules

Bouaita, Bilal; Moussaoui, Abdelouahab; Bachari, Nour El Islam

doi:10.3233/jifs-182786

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…Assuming that the transaction database contains N pieces of data, the formula for calculating the support degree is referred in [9,10]:…”

Section: Introduction To Support and Confidencementioning

confidence: 99%

Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm

Wei

2022

International Transactions on Electrical Energy Systems

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As a very common and classic big data (BD) mining algorithm, the association rule data mining (DM) algorithm is often used to determine the internal correlation between different items and set a certain threshold to determine the size of the correlation. However, the traditional association rule algorithm is more suitable for establishing Boolean association rules between different items of different types of data, and hardening the sharp boundaries of the data causes the performance of the association rules to decrease. In order to overcome this shortcoming of classic DM, this article introduces association rules, support and confidence, the Apriori algorithm and fuzzy association rules based on the neutrosophic fuzzy association rule (NFAR). This paper is based on the data set of the supermarket purchase goods database, by drawing a radar chart to describe the correlation between different goods and different item sets support, and confidence calculation based on association rules support. Finally, the association rules are generated. Compared to the results produced by NFAR and ordinary association rules, the accuracy of the NFAR association rules algorithm in small data sets is 88.48%, while the accuracy of traditional association rules algorithm is only 80.87%, nearly 8 percentage points higher. On large data sets, the prediction accuracy of the neutral fuzzy association rules algorithm is 95.68%, while that of the traditional method is only 89.63%. Therefore, the NFAR algorithm can improve the accuracy and effectiveness of DM. This algorithm has great application prospects and development space in big DM and analysis.

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“…Assuming that the transaction database contains N pieces of data, the formula for calculating the support degree is referred in [9,10]:…”

Section: Introduction To Support and Confidencementioning

confidence: 99%

Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm

Wei

2022

International Transactions on Electrical Energy Systems

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“…Before using a large amount of data in prediction models, we use mining association rules and apply the kernel principal component analysis (KPCA) method to extract the features of economic data [17]. KPCA is an extension of PCA using kernels.…”

Section: Macroeconomic Feature Extractionmentioning

confidence: 99%

Design of Macroeconomic Growth Prediction Algorithm Based on Data Mining

Sun

Yang

Miao

2021

Mobile Information Systems

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With the rapid development of information technology and globalization of economy, financial data are being generated and collected at an unprecedented rate. Consequently, there has been a dire need of automated methods for effective and proficient utilization of a substantial amount of financial data to help in investment planning and decision-making. Data mining methods have been employed to discover hidden patterns and estimate future tendencies in financial markets. In this article, an improved macroeconomic growth prediction algorithm based on data mining and fuzzy correlation analysis is presented. This study analyzes the sequence of economic characteristics, reorganizes the spatial structure of economic characteristics, and integrates the statistical information of economic data. Using the optimized Apriori algorithm, the association rules between macroeconomic data are generated. Distinct features are extracted according to association rules using the joint distribution characteristic quantity of macroeconomic time series. Moreover, the Doppler parameter of macroeconomic time series growth prediction is calculated, and the residual analysis method of the regression model is used to predict the growth of macroeconomic data. Experimental results show that the proposed algorithm has better adaptability, less computation time, and higher prediction accuracy of economic data mining.

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“…This approach was introduced to analyze the shopping cart or transaction data [2]. As each database transaction contains all items purchased by a customer, an association rule method identifies attributes or items that are often purchased together and discovers some meaningful dependencies and relationships between items sold for making predictions or decisions [7]. These relations are in the form of a rule: if X, then Y (X→Y (75%)), where X is the condition of the rule and Y is its conclusion.…”

Section: Introductionmentioning

confidence: 99%

“…Each item or itemset is characterized by its support (Supp) [7]. The Apriori algorithm [11] minimizes calculations and the number of itemsets based on the support value.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Optimizing the Extraction of Association Rules

Bouaita

Beghriche

Kout

et al. 2023

Eng. Technol. Appl. Sci. Res.

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Association rule methods are among the most used approaches for Knowledge Discovery in Databases (KDD), as they allow discovering and extracting hidden meaningful relationships between attributes or items in large datasets in the form of rules. Algorithms to extract these rules require considerable time and large memory spaces. This paper presents an algorithm that decomposes this complex problem into subproblems and processes items by category according to their support. Very frequent items and fairly frequent items are studied together. To evaluate the performance of the proposed algorithm, it was compared with Eclat and LCMFreq on two actual transactional databases. The experimental results showed that the proposed algorithm was faster in execution time and demonstrated its efficiency in memory consumption.

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Rainfall estimation from MSG images using fuzzy association rules

Cited by 8 publications

References 25 publications

Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm

Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm

Design of Macroeconomic Growth Prediction Algorithm Based on Data Mining

A New Approach for Optimizing the Extraction of Association Rules

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