Uncertainty mode selection in categorical clustering using the rough set theory

Naouali, Sami; Salem, Semeh Ben; Chtourou, Zied

doi:10.1016/j.eswa.2020.113555

Cited by 16 publications

(5 citation statements)

References 32 publications

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“…Similar to J. Y. Liang, R. J. Kuo has conducted studies on metaheuristic-based clustering in collaboration with T. P. Q. Nguyen [2,[53][54][55]60,69]. Other authors, such as S. Salem, S. Naouali, and Z. Chtourou, have also worked on rough-set clustering [64,[70][71][72]. Furthermore, Y. M. Cheung, as the second author, has proposed numerous methods related to distance metrics with Y. Q. Zhang [73][74][75] and H. Jia [58].…”

Section: • Publication Titles and Publishersmentioning

confidence: 98%

“…Another algorithm, known as the density rough K-modes (DRk-M) algorithm [70][71][72], has been proposed to address the issue of random selection during the update of modes in the K-Modes algorithm. The DRk-M algorithm calculates the density of the modes and subsequently applies RST to select the most suitable modes based on the concepts of lower and upper approximations in RST.…”

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confidence: 99%

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Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Cendana,

Kuo

2024

MAKE

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Numerous real-world applications apply categorical data clustering to find hidden patterns in the data. The K-modes-based algorithm is a popular algorithm for solving common issues in categorical data, from outlier and noise sensitivity to local optima, utilizing metaheuristic methods. Many studies have focused on increasing clustering performance, with new methods now outperforming the traditional K-modes algorithm. It is important to investigate this evolution to help scholars understand how the existing algorithms overcome the common issues of categorical data. Using a research-area-based bibliometric analysis, this study retrieved articles from the Web of Science (WoS) Core Collection published between 2014 and 2023. This study presents a deep analysis of 64 articles to develop a new taxonomy of categorical data clustering algorithms. This study also discusses the potential challenges and opportunities in possible alternative solutions to categorical data clustering.

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Section: • Publication Titles and Publishersmentioning

confidence: 98%

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confidence: 99%

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Cendana,

Kuo

2024

MAKE

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“…Fuzzy set theory [71] Strong processing capacity Lack of systematicness Rough set theory [72] Without any prior information, the description is objective Limitations: it may not be able to fully and effectively describe uncertain problems [80] e global optimal solution can be obtained without strictly observing the linearity and convexity of the objective function and constraints, and the convergence is good. When there are many variables, the modelling is complex and the calculation speed is slow.…”

Section: Slow Operation Speedmentioning

confidence: 99%

Review on Reactive Power and Voltage Optimization of Active Distribution Network with Renewable Distributed Generation and Time-Varying Loads

et al. 2021

Mathematical Problems in Engineering

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With a high proportion of renewable distributed generation and time-varying load connected to the distribution network, great challenges have appeared in the reactive power optimization control of the active distribution networks. This paper first introduces the characteristics of active distribution networks, the mechanism and research status of wind power, photovoltaic, and other renewable distributed generators, and time-varying loads participating in reactive power and voltage optimization. Then, the paper summarizes the methods of reactive power optimization and voltage regulation of active distribution network, including multi-timescale voltage optimization, coordinated optimization of network reconfiguration and reactive power optimization, coordinated optimization of active and reactive power optimization based on model predictive control, hierarchical and zoning control of reactive power, and voltage and power electronic switch voltage regulation. The pros and cons of the reactive power optimization algorithms mentioned above are summarized. Finally, combined with the development trend of the energy Internet, the future directions of reactive power and voltage control technology in the active distribution network are discussed.

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“…Rough set is found to be extremely suitable for classifying the air pollutant index in Malaysia and Singapore (Wibowo et al, 2018). Furthermore, the theory has been used to various uncertainty handling, and multicriteria decision-making applications (Kazemitash et al, 2021;Naouali et al, 2020;Pal et al, 2018;Saha et al, 2010;Suresh et al, 2012;Tang et al, 2020;Wang & Zhang, 2014;Ye et al, 2021). In our present study, we have used rough set-based "if-then" rules and inverse-decision rules for air-quality prediction and interpreting/explaining the decision-making process, respectively.…”

Section: Introductionmentioning

confidence: 99%

Z-number-based AQI in rough set theoretic framework for interpretation of air quality for different thresholds of PM2.5 and PM10

Dutta

Pal

2022

Environ Monit Assess

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Kolkata has a reputation for being one of the world’s most polluted cities, particularly in the post-monsoon months of October, November, and December. Diwali, a Hindu festival, coincides with these months where a large number of firecrackers are set off followed by high emissions of air pollutants. As a result, the air quality index (AQI) deteriorates to “very poor” (301 ≤ AQI ≤ 400) and “poor” (201 ≤ AQI ≤ 300) categories. This situation stays for several days to a month. The present study aims to identify the thresholds for PM 2.5 and PM 10 that cause the AQI of Kolkata to deteriorate to “very poor” and “poor.” For this purpose, we have used a rough set theory-based condition-decision support system to predict the aforementioned categories of AQI. We have developed a Z-number-based novel quantification measure of semantic information of AQI to assess the reliability of the outcomes, as generated from the condition-decision-based decision rules, during post-monsoon season. The result reveals the best possible forecast of AQI with linguistic summarization of the reliability or confidence for different threshold ranges of PM 10 and PM 2.5 . Inverse-decision rules based on rough set theory are utilized to justify and validate the forecasts. The explainability of the condition-decision support system is demonstrated/visualized using a flow graph that maps rough-rule-based different decision paths between input and output with strength, certainty, and coverage. The investigation resulted in an advanced intelligent environmental decision support system (IEDSS) for air-quality prediction.

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Uncertainty mode selection in categorical clustering using the rough set theory

Cited by 16 publications

References 32 publications

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Review on Reactive Power and Voltage Optimization of Active Distribution Network with Renewable Distributed Generation and Time-Varying Loads

Z-number-based AQI in rough set theoretic framework for interpretation of air quality for different thresholds of PM2.5 and PM10

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