Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition 2019
DOI: 10.1145/3373509.3373521
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Clustering Data Stream with Rough Set

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“…The authors developed a DLRSA model (feature extraction based on deep learning and situation assessment based on rough set analysis named SARSA), which can be treated as an extension of the cyberspace situational awareness (CSA) model. However, in comparison with the results of, e.g., Li and Shen (2020), Chen et al (2020a) or Hassan (2017), RST can be used in the classical way (Pawlak, 1991) andserve as a technique for realizing a cyberspace situation assessment A unified framework of dynamic three-way probabilistic rough sets • a novel matrix approach is investigated D 'eer et al (2016) Rough sets and covering-based rough sets Qiana et al (2017) Incremental rough set approach for hierarchical multicriteria classification Hu and Wang (2008) Algorithms for computing positive region and attribute core based on divide and conquer method Wan and Li (2019) Clustering data stream with rough sets • introduces upper and lower approximations in rough sets to describe the uncertainty of the data stream • lets a time decay model to describe the evolution of data flow Lu et al (2019) Method for big data sets of high-resolution earth observation images Kune (2014) Genetic algorithm based data-aware group scheduling for big data clouds Sachin and Shubhangi (2015) Parallel RS and MapReduce from big data Tang et al (2019) Granular computing based online public opinion Bello and Falcon (2017), Pal (2020) Discretization method for high-resolution remote sensing big data Thuy and Wongthanavasu (2021) Attribute selection method for high-dimensional mixed decision tables • introduces a new concept of stripped neighbourhood covers to reduce unnecessary tolerance classes from the original cover to enhance the process of transforming information into knowledge . It is worth mentioning that the concepts from the classical rough set approach were adopted by the authors into specific needs, like the set of monitoring data in DLRSA which can be perceived as a conditional attribute set, and the set of situation values would be regarded as a resulting attribute set .…”
Section: 2mentioning
confidence: 99%
“…The authors developed a DLRSA model (feature extraction based on deep learning and situation assessment based on rough set analysis named SARSA), which can be treated as an extension of the cyberspace situational awareness (CSA) model. However, in comparison with the results of, e.g., Li and Shen (2020), Chen et al (2020a) or Hassan (2017), RST can be used in the classical way (Pawlak, 1991) andserve as a technique for realizing a cyberspace situation assessment A unified framework of dynamic three-way probabilistic rough sets • a novel matrix approach is investigated D 'eer et al (2016) Rough sets and covering-based rough sets Qiana et al (2017) Incremental rough set approach for hierarchical multicriteria classification Hu and Wang (2008) Algorithms for computing positive region and attribute core based on divide and conquer method Wan and Li (2019) Clustering data stream with rough sets • introduces upper and lower approximations in rough sets to describe the uncertainty of the data stream • lets a time decay model to describe the evolution of data flow Lu et al (2019) Method for big data sets of high-resolution earth observation images Kune (2014) Genetic algorithm based data-aware group scheduling for big data clouds Sachin and Shubhangi (2015) Parallel RS and MapReduce from big data Tang et al (2019) Granular computing based online public opinion Bello and Falcon (2017), Pal (2020) Discretization method for high-resolution remote sensing big data Thuy and Wongthanavasu (2021) Attribute selection method for high-dimensional mixed decision tables • introduces a new concept of stripped neighbourhood covers to reduce unnecessary tolerance classes from the original cover to enhance the process of transforming information into knowledge . It is worth mentioning that the concepts from the classical rough set approach were adopted by the authors into specific needs, like the set of monitoring data in DLRSA which can be perceived as a conditional attribute set, and the set of situation values would be regarded as a resulting attribute set .…”
Section: 2mentioning
confidence: 99%