Adaptive weighted fuzzy clustering based on intra-cluster data divergence

Wu, Ziheng; Zhao, Yuxiao; Wang, Wenyan; Liu, Cong

doi:10.1016/j.neucom.2023.126550

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…Once all the required components for the village clustering procedure are gathered, the final stage involves computing the objective value. The Fuzzy C-means clustering approach is an effective clustering method that has been successfully applied to a number of real-world problems [44]. This computation is executed with predefined parameters: weight = 2, iterations = 100, and an epsilon value set to 0.000001.…”

Section: Determining Membership Degreementioning

confidence: 99%

A Novelty Decision-Making Based on Hybrid Indexing, Clustering, and Classification Methodologies: An Application to Map the Relevant Experts Against the Rural Problem

Faisal,

Rahman,

Mulyadi

et al. 2024

Decis. Mak. Appl. Manag. Eng.

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Goals (SDGs) within intricate rural contexts holds paramount significance. Sustainable rural development holds profound significance for both developed and developing nations. This study was conducted to develop a methodological framework for placing experts with strategically relevant competencies to meet the specific needs of villages, thus enabling the practical application of their expertise in generating innovative solutions to problems in a village. This study entails several pivotal phases. Firstly, it constructs a Community Standard of Living Index (CSLI) using Delphi and Rank Reciprocal (RR). Secondly, it establishes village clustering through a hybrid Fuzzy C-Means (FCM), Self-Organizing Map (SOM), and Xie-Beni (XB). Thirdly, a classification of village development levels is created using Tsukamoto and Smallest of Maximum (SM). Finally, recommendations for placing experts in villages, aligning their skills with identified needs using the Cosine Similarity (CS). The results obtained are compared with factual data of each village to obtain relevant conclusions, where an accuracy value of 0.95 indicates a high success rate in the test results of the proposed technique. This study has the potential to significantly enhance decision-making by introducing opportunities for the development of hybrid methodologies in expert mapping for rural issues.

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Section: Determining Membership Degreementioning

confidence: 99%

A Novelty Decision-Making Based on Hybrid Indexing, Clustering, and Classification Methodologies: An Application to Map the Relevant Experts Against the Rural Problem

Faisal,

Rahman,

Mulyadi

et al. 2024

Decis. Mak. Appl. Manag. Eng.

View full text Add to dashboard Cite

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A robust multi-view knowledge transfer-based rough fuzzy C-means clustering algorithm

Zhao,

Yang,

Liu

et al. 2024

Complex Intell. Syst.

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Rough fuzzy clustering algorithms have received extensive attention due to the excellent ability to handle overlapping and uncertainty of data. However, existing rough fuzzy clustering algorithms generally consider single view clustering, which neglects the clustering requirements of multiple views and results in the failure to identify diverse data structures in practical applications. In addition, rough fuzzy clustering algorithms are always sensitive to the initialized cluster centers and easily fall into local optimum. To solve the above problems, the multi-view and transfer learning are introduced into rough fuzzy clustering and a robust multi-view knowledge transfer-based rough fuzzy c-means clustering algorithm (MKT-RFCCA) is proposed in this paper. First, multiple distance metrics are adopted as multiple views to effectively recognize different data structures, and thus positively contribute to clustering. Second, a novel multi-view transfer-based rough fuzzy clustering objective function is constructed by using fuzzy memberships as transfer knowledge. This objective function can fully explore and utilize the potential information between multiple views and characterize the uncertainty information. Then, combining the statistical information of color histograms, an initialized centroids selection strategy is presented for image segmentation to overcome the instability and sensitivity caused by the random distribution of the initialized cluster centers. Finally, to reduce manual intervention, a distance-based adaptive threshold determination mechanism is designed to determine the threshold parameter for dividing the lower approximation and boundary region of rough fuzzy clusters during the iteration process. Experiments on synthetic datasets, real-world datasets, and noise-contaminated Berkeley and Weizmann images show that MKT-RFCCA obtains favorable clustering results. Especially, it provides satisfactory segmentation results on images with different types of noise and preserves more specific detail information of images.

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Prediction of rate of penetration based on drilling conditions identification for drilling process

Yang,

Wu,

et al. 2024

Neurocomputing

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Adaptive weighted fuzzy clustering based on intra-cluster data divergence

Cited by 5 publications

References 28 publications

A Novelty Decision-Making Based on Hybrid Indexing, Clustering, and Classification Methodologies: An Application to Map the Relevant Experts Against the Rural Problem

A Novelty Decision-Making Based on Hybrid Indexing, Clustering, and Classification Methodologies: An Application to Map the Relevant Experts Against the Rural Problem

A robust multi-view knowledge transfer-based rough fuzzy C-means clustering algorithm

Prediction of rate of penetration based on drilling conditions identification for drilling process

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