Novel fuzzy classification approaches based on optimisation of association rules

Kumar, Pullela S V V S R; Maddireddi, L R D Prasad; Lakshmi, V. Anantha; Kumar, Dirisala J. Nagendra

doi:10.1109/icatcct.2016.7911955

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…Typical works are [31], where the authors analyze the stock of a supermarket, and [32], where authors predict admission decisions by students. Additionally, works such as [33] have searched relationships between extracted rules (association rules) and other techniques such as fuzzy classification.…”

Section: Methodology Based On Data Mining To Improve the Fusion Prmentioning

confidence: 99%

Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets

Padilla¹,

García

Molina

2019

Sensors

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In this paper, a monitoring system of agricultural production is modeled as a Data Fusion System (data from local fairs and meteorological data). The proposal considers the particular information of sales in agricultural markets for knowledge extraction about the associations among them. This association knowledge is employed to improve predictions of sales using a spatial prediction technique, as shown with data collected from local markets of the Andean region of Ecuador. The commercial activity in these markets uses Alternative Marketing Circuits (CIALCO). This market platform establishes a direct relationship between producer and consumer prices and promotes direct commercial interaction among family groups. The problem is presented first as a general fusion problem with a network of spatially distributed heterogeneous data sources, and is then applied to the prediction of products sales based on association rules mined in available sales data. First, transactional data is used as the base to extract the best association rules between products sold in different local markets, knowledge that allows the system to gain a significant improvement in prediction accuracy in the spatial region considered.

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Section: Methodology Based On Data Mining To Improve the Fusion Prmentioning

confidence: 99%

Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets

Padilla¹,

García

Molina

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…A series of studies conducted in various fields of science try to use the rules of association as a criterion to establish future estimates, so we can see some works such as [5], whose authors analyze the stock of a supermarket, or [6], to predict admission decisions for students. In works like [7], a relationship between association rules and a fuzzy classification is established.…”

Section: Related Workmentioning

confidence: 99%

Data Association Methodology to Improve Spatial Predictions in Alternative Marketing Circuits in Ecuador

Padilla¹,

García²

2018

Computational Intelligence and Neuroscience

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This work proposes a methodology that reduces the error of future estimations in commercialization based on multivariate spatial prediction techniques (cokriging) considering the products with strong associations. It is based on the Apriori algorithm to find association rules in sales of agricultural products of local markets. Results show the improvement in spatial prediction accuracy after using the best association rules.

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Improving Forecasting Using Information Fusion in Local Agricultural Markets

Padilla¹,

García

Molina

2018

Lecture Notes in Computer Science

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Novel fuzzy classification approaches based on optimisation of association rules

Cited by 7 publications

References 9 publications

Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets

Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets

Data Association Methodology to Improve Spatial Predictions in Alternative Marketing Circuits in Ecuador

Improving Forecasting Using Information Fusion in Local Agricultural Markets

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