Literature review and analysis on big data stream classification techniques

Srivani, B.; Sandhya, N.; Rani, B. Padmaja

doi:10.3233/kes-200042

Cited by 9 publications

(3 citation statements)

References 33 publications

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“…KNN, which is a nonparametric method for computational training and testing samples in a dataset, is often used for classification [74]. It classifies the input values in the existing data into the k-nearest samples.…”

Section: Supplementary Materialsmentioning

confidence: 99%

Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning

Li,

Huang,

Zhang

et al. 2024

Land

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The visual perception of landscape spaces between residences in cold regions is important for public health. To compensate for the existing research ignoring the cold snow season’s influence, this study selected two types of outdoor landscape space environments in non-snow and snow seasons as research objects. An eye tracker combined with a semantic differential (SD) questionnaire was used to verify the feasibility of the application of virtual reality technology, screen out the gaze characteristics in the landscape space, and reveal the design factors related to landscape visual perception. In the snow season, the spatial aspect ratio (SAR), building elevation saturation (BS), and grass proportion in the field of view (GP) showed strong correlations with the landscape visual perception scores (W). In the non-snow season, in addition to the above three factors, the roof height difference (RHD), tall-tree height (TTH), and hue contrast (HC) also markedly influenced W. The effects of factors on W were revealed in immersive virtual environment (IVE) orthogonal experiments, and the genetic algorithm (GA) and k-nearest neighbor algorithm (KNN) were combined to optimize the environmental factors. The optimized threshold ranges in the non-snow season environment were SAR: 1.82–2.15, RHD: 10.81–20.09 m, BS: 48.53–61.01, TTH: 14.18–18.29 m, GP: 0.12–0.15, and HC: 18.64–26.83. In the snow season environment, the optimized threshold ranges were SAR: 2.22–2.54, BS: 68.47–82.34, and GP: 0.1–0.14.

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Section: Supplementary Materialsmentioning

confidence: 99%

Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning

Li,

Huang,

Zhang

et al. 2024

Land

View full text Add to dashboard Cite

show abstract

“…Take these two points as the center of the cluster for cluster calculation to obtain two cluster sets. Calculate the distance between the data elements in the three set classes and each cluster center, continue the cluster analysis, and then obtain four cluster sets [31]. v. Based on the above calculation results, the cluster numbers of different cluster centers are listed, as shown in Table 4.…”

Section: Number Region Specificationsmentioning

confidence: 99%

Intelligent analysis and processing technology of big data based on clustering algorithm

Zhang

Cao

Gao³

et al. 2022

IJCAI

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An attribute category clustering method based on hierarchical clustering is proposed in order to study the big data intelligent analysis and processing technology. The proposed model combines the attribute categories with similar fault type distribution, reduces the data dimension, and binarizes it. To address the problem of more missing values of continuous data, a data completion method based on attribute distribution function is adopted. Through the perspective of selection and estimation of project unit price in construction enterprises, this paper summarizes the data mining process facing the characteristics of project cost data, and puts forward the method of analyzing and processing project cost data based on clustering algorithm. Finally, the processed data sets are subjected to bottom-up hierarchical clustering analysis, and finally the ideal analysis results can be obtained. The experimental results show that the preprocessing method based on attribute clustering proposed in this paper can effectively merge attributes, reduce the dimension after binary transformation and effectively reduce the amount of data under the condition of ensuring data information.Povzetek: S hierarhičnim gručenjem je narejena inteligentna analiza velikih podatkov.

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“…Srivani and others studied the research on accurate identification in targeted poverty alleviation. e most important thing in exploring accurate identification is to explore the research on accurate identification through Multidimensional Poverty Measurement [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Statistics and Analysis of Targeted Poverty Alleviation Information Integrated with Big Data Mining Algorithm

Mei-zhen

Gao

2022

Security and Communication Networks

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To gain a more comprehensive and systematic understanding of the impact of government assistance to poor households on poverty reduction targets, a targeted poverty alleviation information statistics and analysis integrated with big data mining algorithm is proposed. Combined with the big data knowledge of the new era, according to the machine learning (ML) pipeline module in spark, a big data computing framework, combined with known data mining algorithms, massive sample data are used to replace random stratified sampling data for modeling and analysis, and random forest model, logistic model, and newly proposed waterfall model are constructed for poor households. Finally, through the comparative evaluation of several poor household identification models, the results show that when 100 real data test the accuracy of the three poor household models, the random forest model and logistic model are slightly reduced, which are 82% and 72%, respectively, but the waterfall model is basically unchanged, which is 83%, and the three models have little change. The new waterfall design proposed in this article has the advantage of a high percentage of sample reuse and can effectively prevent overfitting, and there is no need for massive data. It is a stable and reliable new model. The combination of targeted poverty reduction algorithms and big information technology and mining data can get the most common causes more accurate and convincing results. The right rib trunk and rib are often separated from the common cause because of the population.

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Literature review and analysis on big data stream classification techniques

Cited by 9 publications

References 33 publications

Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning

Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning

Intelligent analysis and processing technology of big data based on clustering algorithm

Statistics and Analysis of Targeted Poverty Alleviation Information Integrated with Big Data Mining Algorithm

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