Rule Generation Based on Novel Kernel Intuitionistic Fuzzy Rough Set Model

Lin, Kuo-Ping; Hung, Kuo-Chen; Lin, Ching-Lin

doi:10.1109/access.2018.2809456

Cited by 14 publications

(6 citation statements)

References 44 publications

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“…The specific expressions of W jk and η ik are as shown in the formula (19), the formula (20), and the formula (22). Using the Lagrange Multiplier method [41], the target function can be rewritten as follows:…”

Section: Improved Ifcm Form Based On Distribution Informationmentioning

confidence: 99%

An Intuitionistic Kernel-Based Fuzzy C-Means Clustering Algorithm With Local Information for Power Equipment Image Segmentation

Chen

Wang

2020

IEEE Access

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With the transformation of the national energy and power sector, the steady advancement of intelligent power grid construction and the continuous improvement of the Ubiquitous Power Internet of Things technology framework, it has further requirements for realizing state comprehensive awareness and efficient processing of information data, and has been widely used in power equipment. The infrared image recognition technology, which has been widely used in thermal fault diagnosis of power equipment, also requires deeper research. For traditional Intuitionistic Fuzzy C-means (IFCM) algorithm for image segmentation is sensitive to the clustering center lead to low final clustering precision and detail, the time complexity and the high shortage. The paper puts forward a kind of applicable to power equipment of the infrared image segmentation based on space distribution information of Intuitionistic Fuzzy clustering algorithm. Non-target objects with high intensity and uneven image intensity in infrared image have strong interference to image segmentation. The proposed algorithm can effectively suppress the interference. Firstly, the gaussian model is introduced into the global spatial distribution information of power equipment to improve the IFCM. Secondly, the spatial operator optimization membership function of local spatial information is used to solve the problem of edge blurring and uneven image intensity. Through experiments on the data set containing 300 infrared images of power equipment, the relative regional error rate is about 10%, which is less affected by the change of fuzzy factor m. The effectiveness and applicability of this algorithm are verified, which is obviously better than other comparison algorithms. INDEX TERMS Intuitionistic fuzzy clustering, infrared image, Gaussian model, local information.

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Section: Improved Ifcm Form Based On Distribution Informationmentioning

confidence: 99%

An Intuitionistic Kernel-Based Fuzzy C-Means Clustering Algorithm With Local Information for Power Equipment Image Segmentation

Chen

Wang

2020

IEEE Access

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“…To condense attribute sets in extensive intuitive fuzzy information systems, a rule base with minimum size and optimal generation configuration time and storage space can be achieved by utilizing genetic algorithms and intuitive fuzzy rough sets [12]. Several researchers have developed a fuzzy rule generation model with the rough set method as the Kernel intuitionistic fuzzy rough set model (KIFRS) method [13] and proposed the integration of fuzzy logic in the application of the fuzzy rule generation method to the functional resonance analysis (FRAM) to evaluate deicing operations from a systemic plane perspective, where the process of integrating fuzzy logic with a large number of input variables produces many rules. To further refine the proposal, the rough set method is used as a data mining tool to generate and reduce the number of rules in classification [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

Development of the fuzzy grid partition methods in generating fuzzy rules for the classification of data set

Marbun,

Sitompul,

Nababan

et al. 2024

Bulletin EEI

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The main weakness of complex and sizeable fuzzy rule systems is the complexity of data interpretation in terms of classification. Classification interpretation can be affected by reducing rules and removing important rules for several reasons. Based on the results of experiments using the fuzzy grid partition (FGP) approach for high-dimensional data, the difficulty in generating many fuzzy rules still increases exponentially as the number of characteristics increases. The solution to this problem is a hybrid method that combines the advantages of the rough set method and the FGP method, which is called the fuzzy grid partition rough set (FGPRS) method. In the Irish data, the rough set approach reduces the number of characteristics and objects so that data with excessive values can be minimized, and the fuzzy rules produced using the FGP method are more concise. The number of fuzzy rules produced using the FGPRS method at K=2 is 50%; at K=K+1, it is reduced by 66.7% and at K=2 K, it is reduced by 75%. Based on the findings of the data collection classification test, the FGPRS method has a classification accuracy rate of 83.33%, and all data can be classified.

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“…Clustering methods are often used to support data-driven decision making in numerous domains such as Businesses (e.g., market dynamic analysis) [6], Healthcare (e.g., protein sequence analysis) [7][8][9], Science (e.g., environmental data analysis) [10], Information Security [11], Computer Networks [12], Image Segmentation [13] and Software Maintenance [14,15]. In data analytics, clustering method lies at the core of successful data analysis tasks such as data summation, classification as well as data reduction, filtering, exploratory data analysis and many more [14,[16][17][18][19]. A variety of cluster analysis methods for numerical data analysis are commonly deployed by organizations.…”

Section: Introductionmentioning

confidence: 99%

Rough set based information theoretic approach for clustering uncertain categorical data

et al. 2022

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Motivation Many real applications such as businesses and health generate large categorical datasets with uncertainty. A fundamental task is to efficiently discover hidden and non-trivial patterns from such large uncertain categorical datasets. Since the exact value of an attribute is often unknown in uncertain categorical datasets, conventional clustering analysis algorithms do not provide a suitable means for dealing with categorical data, uncertainty, and stability. Problem statement The ability of decision making in the presence of vagueness and uncertainty in data can be handled using Rough Set Theory. Though, recent categorical clustering techniques based on Rough Set Theory help but they suffer from low accuracy, high computational complexity, and generalizability especially on data sets where they sometimes fail or hardly select their best clustering attribute. Objectives The main objective of this research is to propose a new information theoretic based Rough Purity Approach (RPA). Another objective of this work is to handle the problems of traditional Rough Set Theory based categorical clustering techniques. Hence, the ultimate goal is to cluster uncertain categorical datasets efficiently in terms of the performance, generalizability and computational complexity. Methods The RPA takes into consideration information-theoretic attribute purity of the categorical-valued information systems. Several extensive experiments are conducted to evaluate the efficiency of RPA using a real Supplier Base Management (SBM) and six benchmark UCI datasets. The proposed RPA is also compared with several recent categorical data clustering techniques. Results The experimental results show that RPA outperforms the baseline algorithms. The significant percentage improvement with respect to time (66.70%), iterations (83.13%), purity (10.53%), entropy (14%), and accuracy (12.15%) as well as Rough Accuracy of clusters show that RPA is suitable for practical usage. Conclusion We conclude that as compared to other techniques, the attribute purity of categorical-valued information systems can better cluster the data. Hence, RPA technique can be recommended for large scale clustering in multiple domains and its performance can be enhanced for further research.

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Rule Generation Based on Novel Kernel Intuitionistic Fuzzy Rough Set Model

Cited by 14 publications

References 44 publications

An Intuitionistic Kernel-Based Fuzzy C-Means Clustering Algorithm With Local Information for Power Equipment Image Segmentation

An Intuitionistic Kernel-Based Fuzzy C-Means Clustering Algorithm With Local Information for Power Equipment Image Segmentation

Development of the fuzzy grid partition methods in generating fuzzy rules for the classification of data set

Rough set based information theoretic approach for clustering uncertain categorical data

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