Bi-Fuzzy Clustering Algorithm by Augmented Granulation-Degranulation Mechanisms

Xu, Kaijie; Nie, Weike

doi:10.1109/siprocess.2019.8868442

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…In granular computing, the process of clustering data through FCM is also referred to as information granulation, representing a granulation mechanism [27]. Information granules are expressed through prototypes and membership matrices.…”

Section: Augmentation Of Fuzzy C-means With Granular Prototypesmentioning

confidence: 99%

Augmentation of Soft Partition with a Granular Prototype Based Fuzzy C-Means

Wang,

Xu,

Wang

2024

Mathematics

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Clustering is a fundamental cornerstone in unsupervised learning, playing a pivotal role in various data mining techniques. The precise and efficient classification of data stands as a central focus for numerous researchers and practitioners alike. In this study, we design an effective soft partition classification method which refines and extends the prototype of the well-known Fuzzy C-Means clustering algorithm. Specifically, the developed scheme employs membership function to extend the prototypes into a series of granular prototypes, thus achieving a deeper revelation of the structure of the data. This process softly divides the data into core and extended parts. The core part can be succinctly encapsulated through several information granules, whereas the extended part lacks discernible geometry and requires formal descriptors (such as membership formulas). Our objective is to develop information granules that shape the core structure within the dataset, delineate their characteristics, and explore the interaction among these granules that result in their deformation. The granular prototypes become the main component of the information granules and provide an optimization space for traditional prototypes. Subsequently, we apply quantum-behaved particle swarm optimization to identify the optimal partition matrix for the data. This optimized matrix significantly enhances the partition performance of the data. Experimental results provide substantial evidence of the effectiveness of the proposed approach.

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Section: Augmentation Of Fuzzy C-means With Granular Prototypesmentioning

confidence: 99%

Augmentation of Soft Partition with a Granular Prototype Based Fuzzy C-Means

Wang,

Xu,

Wang

2024

Mathematics

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From granulation-degranulation mechanisms to fuzzy rule-based models: Augmentation of granular-based models with a double fuzzy clustering

Hanyu

Quan

et al. 2021

IFS

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In this study, we develop a novel clustering with double fuzzy factors to enhance the performance of the granulation-degranulation mechanism, with which a fuzzy rule-based model is designed and demonstrated to be an enhanced one. The essence of the developed scheme is to optimize the construction of the information granules so as to eventually improve the performance of the fuzzy rule-based models. In the design process, a prototype matrix is defined to express the Fuzzy C-Means based granulation-degranulation mechanism in a clear manner. We assume that the dataset degranulated from the formed information granules is equal to the original numerical dataset. Then, a clustering method with double fuzzy factors is derived. We also present a detailed mathematical proof for the proposed approach. Subsequently, on the basis of the enhanced version of the granulation-degranulation mechanism, we design a granular fuzzy model. The whole design is mainly focused on an efficient application of the fuzzy clustering to build information granules used in fuzzy rule-based models. Comprehensive experimental studies demonstrate the performance of the proposed scheme.

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Augmentation of the Reconstruction Performance of Fuzzy C-Means with an Optimized Fuzzification Factor Vector

Pedrycz

2020

Preprint

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Bi-Fuzzy Clustering Algorithm by Augmented Granulation-Degranulation Mechanisms

Cited by 3 publications

References 19 publications

Augmentation of Soft Partition with a Granular Prototype Based Fuzzy C-Means

Augmentation of Soft Partition with a Granular Prototype Based Fuzzy C-Means

From granulation-degranulation mechanisms to fuzzy rule-based models: Augmentation of granular-based models with a double fuzzy clustering

Augmentation of the Reconstruction Performance of Fuzzy C-Means with an Optimized Fuzzification Factor Vector

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