Cloud data processing using granular based weighted concept lattice and Hamming distance

Singh, Prem Kumar

doi:10.1007/s00607-018-0608-7

Cited by 7 publications

(2 citation statements)

References 57 publications

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“…In addition, the real estate industry can also adjust prices appropriately through these influencing factors. In the future work, authors will use other approaches such as weighted concept lattice and Hamming distance, Shannon entropy, and deep learning to do more research for finding out the influencing factors of real estate price [17][18][19].…”

Section: Discussionmentioning

confidence: 99%

Data Analysis on the Influencing Factors of the Real Estate Price

He¹,

Chen²,

Xiao³

et al. 2021

Artificial Intelligence Evolution

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China has carried out a large number of real estate market reforms that change the real estate market demand considerably. At the same time, the real estate price has soared in some cities and has surpassed the spending power of many ordinary people. As the real estate price has received widespread attention from society, it is important to understand what factors affect the real estate price. Therefore, we propose a data analysis method for finding out the influencing factors of real estate prices. The method performs data cleaning and conversion on the used data first. To discretize the real estate price, we use the mean ± standard deviation (SD), mean ± 0.5 SD, and mean ± 2 SD of the price and divide it into three categories as the output variable. Then, we establish the decision tree and random forest model for six different situations for comparison. When the data set is divided into training data (70%) and testing data (30%), it has the highest testing accuracy. In addition, by observing the importance of each input variable, it is found that the main influencing factors of real estate price are cost, interior decoration, location, and status. The results suggest that both the real estate industry and buyers should pay attention to these factors to adjust or purchase real estate.

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Section: Discussionmentioning

confidence: 99%

Data Analysis on the Influencing Factors of the Real Estate Price

He¹,

Chen²,

Xiao³

et al. 2021

Artificial Intelligence Evolution

View full text Add to dashboard Cite

show abstract

“…Unlike other models, for example, [62][63][64], in our proposal the formal contexts represented by objects-attributes relationships are not known. In the theoretical setup that we present, we neither generate nor hypothesize about formal contexts.…”

Section: Relation To Other Methodsmentioning

confidence: 99%

Modeling human thinking about similarities by neuromatrices in the perspective of fuzzy logic

Grobelny

Michalski

Weber

2020

Neural Comput & Applic

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In this work, we propose a new method for modeling human reasoning about objects’ similarities. We assume that similarity depends on perceived intensities of objects’ attributes expressed by natural language expressions such as low, medium, and high. We show how to find the underlying structure of the matrix with intensities of objects’ similarities in the factor-analysis-like manner. The demonstrated approach is based on fuzzy logic and set theory principles, and it uses only maximum and minimum operators. Similarly to classic eigenvector decomposition, we aim at representing the initial linguistic ordinal-scale (LOS) matrix as a max–min product of other LOS matrix and its transpose. We call this reconstructing matrix a neuromatrix because we assume that such a process takes place at the neural level in our brain. We show and discuss on simple, illustrative examples, how the presented way of modeling grasps natural way of reasoning about similarities. The unique characteristics of our approach are treating smaller attribute intensities as less important in making decisions about similarities. This feature is consistent with how the human brain is functioning at a biological level. A neuron fires and passes information further only if input signals are strong enough. The proposal of the heuristic algorithm for finding the decomposition in practice is also introduced and applied to exemplary data from classic psychological studies on perceived similarities between colors and between nations. Finally, we perform a series of simulation experiments showing the effectiveness of the proposed heuristic.

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Bipolar fuzzy concepts reduction using granular-based weighted entropy

Singh

2022

Soft Comput

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Cloud data processing using granular based weighted concept lattice and Hamming distance

Cited by 7 publications

References 57 publications

Data Analysis on the Influencing Factors of the Real Estate Price

Data Analysis on the Influencing Factors of the Real Estate Price

Modeling human thinking about similarities by neuromatrices in the perspective of fuzzy logic

Bipolar fuzzy concepts reduction using granular-based weighted entropy

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