Performance Analysis of Collaborative Data Mining vs Context Aware Data Mining in a Practical Scenario for Predicting Air Humidity

Anton, Carmen Ana; Avram, Anca; Petrovan, Adrian; Matei, Oliviu

doi:10.1007/978-3-030-31362-3_5

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…Costache et al [26] successfully used the gradient boosting trees (GBT) and multilayer perceptron (MLP) method to evaluate the flood potential and to predict flood sensitive areas in the Trotus river basin in Romania. Matei et al [27,28] and Anton et al [29] used various techniques, such as collaborative or context-aware data mining, for predicting the soil moisture in Transylvania, Romania. Wu et al [30] used the gradient boosting decision tree (GBDT) algorithm to predict urban floods in Zhengzhou City.…”

Section: Introductionmentioning

confidence: 99%

Soil Temperature Estimation with Meteorological Parameters by Using Tree-Based Hybrid Data Mining Models

et al. 2020

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The temperature of the soil at different depths is one of the most important factors used in different disciplines, such as hydrology, soil science, civil engineering, construction, geotechnology, ecology, meteorology, agriculture, and environmental studies. In addition to physical and spatial variables, meteorological elements are also effective in changing soil temperatures at different depths. The use of machine-learning models is increasing day by day in many complex and nonlinear branches of science. These data-driven models seek solutions to complex and nonlinear problems using data observed in the past. In this research, decision tree (DT), gradient boosted trees (GBT), and hybrid DT–GBT models were used to estimate soil temperature. The soil temperatures at 5, 10, and 20 cm depths were estimated using the daily minimum, maximum, and mean temperature; sunshine intensity and duration, and precipitation data measured between 1993 and 2018 at Divrigi station in Sivas province in Turkey. To predict the soil temperature at different depths, the time windowing technique was used on the input data. According to the results, hybrid DT–GBT, GBT, and DT methods estimated the soil temperature at 5 cm depth the most successfully, respectively. However, the best estimate was obtained with the DT model at soil depths of 10 and 20 cm. According to the results of the research, the accuracy rate of the models has also increased with increasing soil depth. In the prediction of soil temperature, sunshine duration and air temperature were determined as the most important factors and precipitation was the most insignificant meteorological variable. According to the evaluation criteria, such as Nash-Sutcliffe coefficient, R, MAE, RMSE, and Taylor diagrams used, it is recommended that all three (DT, GBT, and hybrid DT–GBT) data-based models can be used for predicting soil temperature.

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Section: Introductionmentioning

confidence: 99%

Soil Temperature Estimation with Meteorological Parameters by Using Tree-Based Hybrid Data Mining Models

et al. 2020

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“…The previous work [12] concluded that both CADM and CDM techniques offer advantages against the classical data mining approach; the current work makes a step forward and provides a hybrid approach of CADM and CDM as depicted in Figure 1.…”

Section: Combining Cadm and Cdm In A Flexible Architecturementioning

confidence: 97%

“…Collaborative data mining is a technique of approaching a machine learning process that involves completing the data of a studied source with data taken from other similar sources [12]. The objective of the process is to provide better results than the one that only uses the data of the studied source.…”

Section: A Collaborative Data Mining (Cdm)mentioning

confidence: 99%

“…The current study makes a step further and extends the work performed in Reference [12] and analyzes how these two approaches would work in different scenarios for this matter, a new hybrid technique was considered, Scenarios Platform-Collaborative & Context-Aware Data Mining (SP-CCADM), which would allow the testing of more combinations and interactions between CADM and CDM. The proposed model was then applied and validated in a real-life scenario.…”

Section: Introductionmentioning

confidence: 96%

“…Innovation: Context-Aware Data Mining (CADM) and Collaborative Data Mining (CDM) combined process overview, influenced by Reference[12].…”

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confidence: 99%

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Innovative Platform for Designing Hybrid Collaborative & Context-Aware Data Mining Scenarios

et al. 2020

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The process of knowledge discovery involves nowadays a major number of techniques. Context-Aware Data Mining (CADM) and Collaborative Data Mining (CDM) are some of the recent ones. the current research proposes a new hybrid and efficient tool to design prediction models called Scenarios Platform-Collaborative & Context-Aware Data Mining (SP-CCADM). Both CADM and CDM approaches are included in the new platform in a flexible manner; SP-CCADM allows the setting and testing of multiple configurable scenarios related to data mining at once. The introduced platform was successfully tested and validated on real life scenarios, providing better results than each standalone technique—CADM and CDM. Nevertheless, SP-CCADM was validated with various machine learning algorithms—k-Nearest Neighbour (k-NN), Deep Learning (DL), Gradient Boosted Trees (GBT) and Decision Trees (DT). SP-CCADM makes a step forward when confronting complex data, properly approaching data contexts and collaboration between data. Numerical experiments and statistics illustrate in detail the potential of the proposed platform.

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How Noisy and Missing Context Influences Predictions in a Practical Context-Aware Data Mining System

Avram

Matei

Pintea

et al. 2020

Advances in Intelligent Systems and Computing

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Performance Analysis of Collaborative Data Mining vs Context Aware Data Mining in a Practical Scenario for Predicting Air Humidity

Cited by 4 publications

References 6 publications

Soil Temperature Estimation with Meteorological Parameters by Using Tree-Based Hybrid Data Mining Models

Soil Temperature Estimation with Meteorological Parameters by Using Tree-Based Hybrid Data Mining Models

Innovative Platform for Designing Hybrid Collaborative & Context-Aware Data Mining Scenarios

How Noisy and Missing Context Influences Predictions in a Practical Context-Aware Data Mining System

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