Log in

Relevant bibliographies by topics / Fuzzy clustering algorithm / Journal articles

To see the other types of publications on this topic, follow the link: Fuzzy clustering algorithm.

Journal articles on the topic 'Fuzzy clustering algorithm'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Fuzzy clustering algorithm.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Raj, A. Stanley, D. Hudson Oliver, and Y. Srinivas. "Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model." International Journal of Geophysics 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/134834.

Full text

Abstract:

Soft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive. In this paper, fuzzy logic clustering methods are used in the inversion of geoelectrical resistivity data. In order to characterize the subsurface features of the earth one should rely on the true field oriented data validation. This paper supports the field data obtained from the published results and also plays a crucial role in making an interdisciplinary approach to solve complex problems. Three clustering algorithms

APA, Harvard, Vancouver, ISO, and other styles

2

Kanzawa, Yuchi. "Bezdek-Type Fuzzified Co-Clustering Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (2015): 852–60. http://dx.doi.org/10.20965/jaciii.2015.p0852.

Full text

Abstract:

In this study, two co-clustering algorithms based on Bezdek-type fuzzification of fuzzy clustering are proposed for categorical multivariate data. The two proposed algorithms are motivated by the fact that there are only two fuzzy co-clustering methods currently available – entropy regularization and quadratic regularization – whereas there are three fuzzy clustering methods for vectorial data: entropy regularization, quadratic regularization, and Bezdek-type fuzzification. The first proposed algorithm forms the basis of the second algorithm. The first algorithm is a variant of a spherical clu

APA, Harvard, Vancouver, ISO, and other styles

3

Jiang, Zhenni, and Xiyu Liu. "A Novel Consensus Fuzzy K-Modes Clustering Using Coupling DNA-Chain-Hypergraph P System for Categorical Data." Processes 8, no. 10 (2020): 1326. http://dx.doi.org/10.3390/pr8101326.

Full text

Abstract:

In this paper, a data clustering method named consensus fuzzy k-modes clustering is proposed to improve the performance of the clustering for the categorical data. At the same time, the coupling DNA-chain-hypergraph P system is constructed to realize the process of the clustering. This P system can prevent the clustering algorithm falling into the local optimum and realize the clustering process in implicit parallelism. The consensus fuzzy k-modes algorithm can combine the advantages of the fuzzy k-modes algorithm, weight fuzzy k-modes algorithm and genetic fuzzy k-modes algorithm. The fuzzy k

APA, Harvard, Vancouver, ISO, and other styles

4

Wu, Shuangsheng, Jie Lin, Zhenyu Zhang, and Yushu Yang. "Hesitant Fuzzy Linguistic Agglomerative Hierarchical Clustering Algorithm and Its Application in Judicial Practice." Mathematics 9, no. 4 (2021): 370. http://dx.doi.org/10.3390/math9040370.

Full text

Abstract:

The fuzzy clustering algorithm has become a research hotspot in many fields because of its better clustering effect and data expression ability. However, little research focuses on the clustering of hesitant fuzzy linguistic term sets (HFLTSs). To fill in the research gaps, we extend the data type of clustering to hesitant fuzzy linguistic information. A kind of hesitant fuzzy linguistic agglomerative hierarchical clustering algorithm is proposed. Furthermore, we propose a hesitant fuzzy linguistic Boole matrix clustering algorithm and compare the two clustering algorithms. The proposed cluste

APA, Harvard, Vancouver, ISO, and other styles

5

KIM, SU HWAN, SEON WOOK KIM, and TAE WON RHEE. "AN EXTENDED FUZZY CLUSTERING ALGORITHM AND ITS APPLICATION." Journal of Circuits, Systems and Computers 05, no. 02 (1995): 239–59. http://dx.doi.org/10.1142/s0218126695000175.

Full text

Abstract:

For data analyses, it is very important to combine data with similar attribute values into a categorically homogeneous subset, called a cluster, and this technique is called clustering. Generally crisp clustering algorithms are weak in noise, because each datum should be assigned to exactly one cluster. In order to solve the problem, a fuzzy c-means, a fuzzy maximum likelihood estimation, and an optimal fuzzy clustering algorithms in the fuzzy set theory have been proposed. They, however, require a lot of processing time because of exhaustive iteration with an amount of data and their membersh

APA, Harvard, Vancouver, ISO, and other styles

6

Gu, Yi, and Kang Li. "Entropy-Based Multiview Data Clustering Analysis in the Era of Industry 4.0." Wireless Communications and Mobile Computing 2021 (April 30, 2021): 1–8. http://dx.doi.org/10.1155/2021/9963133.

Full text

Abstract:

In the era of Industry 4.0, single-view clustering algorithm is difficult to play a role in the face of complex data, i.e., multiview data. In recent years, an extension of the traditional single-view clustering is multiview clustering technology, which is becoming more and more popular. Although the multiview clustering algorithm has better effectiveness than the single-view clustering algorithm, almost all the current multiview clustering algorithms usually have two weaknesses as follows. (1) The current multiview collaborative clustering strategy lacks theoretical support. (2) The weight of

APA, Harvard, Vancouver, ISO, and other styles

7

Pal, Sankar K., and Sushmita Mitra. "Fuzzy dynamic clustering algorithm." Pattern Recognition Letters 11, no. 8 (1990): 525–35. http://dx.doi.org/10.1016/0167-8655(90)90021-s.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Yang, Can-Ming, Ye Liu, Yi-Ting Wang, et al. "A Novel Adaptive Kernel Picture Fuzzy C-Means Clustering Algorithm Based on Grey Wolf Optimizer Algorithm." Symmetry 14, no. 7 (2022): 1442. http://dx.doi.org/10.3390/sym14071442.

Full text

Abstract:

Over the years, research on fuzzy clustering algorithms has attracted the attention of many researchers, and they have been applied to various areas, such as image segmentation and data clustering. Various fuzzy clustering algorithms have been put forward based on the initial Fuzzy C-Means clustering (FCM) with Euclidean distance. However, the existing fuzzy clustering approaches ignore two problems. Firstly, clustering algorithms based on Euclidean distance have a high error rate, and are more sensitive to noise and outliers. Secondly, the parameters of the fuzzy clustering algorithms are har

APA, Harvard, Vancouver, ISO, and other styles

9

Wu, Chengmao, and Siyu Zhou. "Robust Harmonic Fuzzy Partition Local Information C-Means Clustering for Image Segmentation." Symmetry 16, no. 10 (2024): 1370. http://dx.doi.org/10.3390/sym16101370.

Full text

Abstract:

Considering the shortcomings of Ruspini partition-based fuzzy clustering in revealing the intrinsic correlation between different classes, a series of harmonic fuzzy local information C-means clustering for noisy image segmentation are proposed. Firstly, aiming at the shortage of Zadeh’s fuzzy sets, a new concept of generalized harmonic fuzzy sets is originally introduced and the corresponding harmonic fuzzy partition is further defined. Then, based on the concept of symmetric harmonic partition, a new harmonic fuzzy local information C-means clustering (HLICM) is proposed and the local conver

APA, Harvard, Vancouver, ISO, and other styles

10

Bouzbida, Mohamed, Lassad Hassine, and Abdelkader Chaari. "Robust Kernel Clustering Algorithm for Nonlinear System Identification." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/2427309.

Full text

Abstract:

In engineering field, it is necessary to know the model of the real nonlinear systems to ensure its control and supervision; in this context, fuzzy modeling and especially the Takagi-Sugeno fuzzy model has drawn the attention of several researchers in recent decades owing to their potential to approximate nonlinear behavior. To identify the parameters of Takagi-Sugeno fuzzy model several clustering algorithms are developed such as the Fuzzy C-Means (FCM) algorithm, Possibilistic C-Means (PCM) algorithm, and Possibilistic Fuzzy C-Means (PFCM) algorithm. This paper presents a new clustering algo

APA, Harvard, Vancouver, ISO, and other styles

11

Abdzaid Atiyah, Israa, Adel Mohammadpour, and S. Mahmoud Taheri. "KC-Means: A Fast Fuzzy Clustering." Advances in Fuzzy Systems 2018 (June 3, 2018): 1–8. http://dx.doi.org/10.1155/2018/2634861.

Full text

Abstract:

A novel hybrid clustering method, named KC-Means clustering, is proposed for improving upon the clustering time of the Fuzzy C-Means algorithm. The proposed method combines K-Means and Fuzzy C-Means algorithms into two stages. In the first stage, the K-Means algorithm is applied to the dataset to find the centers of a fixed number of groups. In the second stage, the Fuzzy C-Means algorithm is applied on the centers obtained in the first stage. Comparisons are then made between the proposed and other algorithms in terms of time processing and accuracy. In addition, the mentioned clustering algo

APA, Harvard, Vancouver, ISO, and other styles

12

Hou, Li Bo. "Improved Fuzzy FCM-LI Algorithm." Advanced Materials Research 765-767 (September 2013): 670–73. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.670.

Full text

Abstract:

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fu

APA, Harvard, Vancouver, ISO, and other styles

13

Min, Min. "Study of Combined Fuzzy Clustering Algorithm Based on F-Statistics Hierarchy Clustering." Applied Mechanics and Materials 29-32 (August 2010): 802–8. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.802.

Full text

Abstract:

On analyzing the common problems in fuzzy clustering algorithms, we put forward the combined fuzzy clustering one, which will automatically generate a reasonable clustering numbers and initial cluster center. This clustering algorithm has been tested by real evaluation data of teaching designs. The result proves that the combined fuzzy clustering based on F-statistic is more effective.

APA, Harvard, Vancouver, ISO, and other styles

14

Nomura, Tomoki, and Yuchi Kanzawa. "Two Fuzzy Clustering Algorithms Based on ARMA Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 28, no. 6 (2024): 1251–62. http://dx.doi.org/10.20965/jaciii.2024.p1251.

Full text

Abstract:

This study proposes two fuzzy clustering algorithms based on autoregressive moving average (ARMA) model for series data. The first, referred to as Tsallis entropy-regularized fuzzy c-ARMA model (TFCARMA), is created from k-ARMA, a conventional hard clustering algorithm for series data. TFCARMA is motivated by the relationship between the two clustering algorithms for vectorial data: k-means and Tsallis entropy-regularized fuzzy c-means. The second, referred to as q-divergence-based fuzzy c-ARMA model (QFCARMA), is created from ARMA mixtures, a conventional probabilistic clustering algorithm fo

APA, Harvard, Vancouver, ISO, and other styles

15

Yang, Ying, Haoyu Chen, and Haoshen Wu. "A generalized fuzzy clustering framework for incomplete data by integrating feature weighted and kernel learning." PeerJ Computer Science 9 (October 5, 2023): e1600. http://dx.doi.org/10.7717/peerj-cs.1600.

Full text

Abstract:

Missing data presents a challenge to clustering algorithms, as traditional methods tend to pad incomplete data first before clustering. To combine the two processes of padding and clustering and improve the clustering accuracy, a generalized fuzzy clustering framework is proposed based on optimal completion strategy (OCS) and nearest prototype strategy (NPS) with four improved algorithms developed. Feature weights are introduced to reduce outliers’ influence on the cluster centers, and kernel functions are used to solve the linear indistinguishability problem. The proposed algorithms are evalu

APA, Harvard, Vancouver, ISO, and other styles

16

Shi, Maolin, Zihao Wang, and Lizhang Xu. "A fuzzy clustering algorithm based on hybrid surrogate model." Journal of Intelligent & Fuzzy Systems 42, no. 3 (2022): 1963–76. http://dx.doi.org/10.3233/jifs-211340.

Full text

Abstract:

Data clustering based on regression relationship is able to improve the validity and reliability of the engineering data mining results. Surrogate models are widely used to evaluate the regression relationship in the process of data clustering, but there is no single surrogate model that always performs the best for all the regression relationships. To solve this issue, a fuzzy clustering algorithm based on hybrid surrogate model is proposed in this work. The proposed algorithm is based on the framework of fuzzy c-means algorithm, in which the differences between the clusters are evaluated by

APA, Harvard, Vancouver, ISO, and other styles

17

Bashir, Muhammad Adnan, Tabasam Rashid, and Muhammad Salman Bashir. "Generalized Ordered Intuitionistic Fuzzy C-Means Clustering Algorithm Based on PROMETHEE and Intuitionistic Fuzzy C-Means." International Journal of Intelligent Systems 2023 (September 22, 2023): 1–21. http://dx.doi.org/10.1155/2023/6686446.

Full text

Abstract:

The problem of ordered clustering in the context of decision-making with multiple criteria has garnered significant interest from researchers in the field of management science and operational research. In real-world scenarios, the datasets often exhibit imprecision or uncertainty, which can lead to suboptimal ordered-clustering outcomes. However, the intuitionistic fuzzy c-means (IFCM) clustering algorithm enhances the accuracy and effectiveness of decision-making processes by effectively handling uncertain dataset information for clustering. Therefore, we propose a new clustering algorithm,

APA, Harvard, Vancouver, ISO, and other styles

18

Lai, Jim Z. C., Eric Y. T. Juan, and Franklin J. C. Lai. "Rough clustering using generalized fuzzy clustering algorithm." Pattern Recognition 46, no. 9 (2013): 2538–47. http://dx.doi.org/10.1016/j.patcog.2013.02.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Agrawal, Anmol, B. K. Tripathy, and Ramkumar Thirunavukarasu. "An Improved Fuzzy Adaptive Firefly Algorithm-Based Hybrid Clustering Algorithms." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, Supp02 (2021): 259–78. http://dx.doi.org/10.1142/s0218488521400146.

Full text

Abstract:

The implication of firefly and fuzzy firefly optimization algorithms has been greatly witnessed in clustering techniques and extensively used in applications such as Image segmentation. Parameters such as step factor and attractiveness have been kept constant in these algorithms, which affect the convergence rate and accuracy of the clustering process. Though fuzzy adaptive firefly algorithm tackled this problem by making those parameters an adaptive one, issues such as low convergence rate, and provision of non-optimal solutions are still there. To tackle these issues, this paper proposed a n

APA, Harvard, Vancouver, ISO, and other styles

20

M., Nithya, K. Balasubramaium, and Senthil S. "MST Initialization Based Intuitionistic Fuzzy c Means Clustering Using LINEX Hellinger Distance and Its Applications." Journal of Electrical and Electronic Engineering 12, no. 2 (2024): 36–47. http://dx.doi.org/10.11648/j.jeee.20241202.12.

Full text

Abstract:

Due to the uncertainty and fuzziness of information, the traditional clustering analysis method sometimes cannot meet the requirement in practice. The clustering method based on intuitionistic fuzzy set has attracted more and more scholars attention nowadays. This paper discusses the intuitionistic fuzzy C-means clustering algorithm. There are a number of clustering techniques developed in the past using different distance/similarity measure. In this paper, we proposed a improved edge density minimal spanning tree initilization method using LINEX hellinger distance based weighted LINEX intuiti

APA, Harvard, Vancouver, ISO, and other styles

21

Xia, Hong, Qingyi Dong, Hui Gao, Yanping Chen, and ZhongMin Wang. "Service Partition Method Based on Particle Swarm Fuzzy Clustering." Wireless Communications and Mobile Computing 2021 (December 8, 2021): 1–12. http://dx.doi.org/10.1155/2021/7225552.

Full text

Abstract:

It is difficult to accurately classify a service into specific service clusters for the multirelationships between services. To solve this problem, this paper proposes a service partition method based on particle swarm fuzzy clustering, which can effectively consider multirelationships between services by using a fuzzy clustering algorithm. Firstly, the algorithm for automatically determining the number of clusters is to determine the number of service clusters based on the density of the service core point. Secondly, the fuzzy c -means combined with particle swarm optimization algorithm to fi

APA, Harvard, Vancouver, ISO, and other styles

22

Yang, Dandan. "Fuzzy Covering-Based Three-Way Clustering." Mathematical Problems in Engineering 2020 (July 31, 2020): 1–10. http://dx.doi.org/10.1155/2020/2901210.

Full text

Abstract:

This paper investigates the three-way clustering involving fuzzy covering, thresholds acquisition, and boundary region processing. First of all, a valid fuzzy covering of the universe is constructed on the basis of an appropriate fuzzy similarity relation, which helps capture the structural information and the internal connections of the dataset from the global perspective. Due to the advantages of valid fuzzy covering, we explore the valid fuzzy covering instead of the raw dataset for RFCM algorithm-based three-way clustering. Subsequently, from the perspective of semantic interpretation of b

APA, Harvard, Vancouver, ISO, and other styles

23

Martino, Ferdinando Di, and Salvatore Sessa. "A New Validity Index Based on Fuzzy Energy and Fuzzy Entropy Measures in Fuzzy Clustering Problems." Entropy 22, no. 11 (2020): 1200. http://dx.doi.org/10.3390/e22111200.

Full text

Abstract:

Two well-known drawbacks in fuzzy clustering are the requirement of assigning in advance the number of clusters and random initialization of cluster centers. The quality of the final fuzzy clusters depends heavily on the initial choice of the number of clusters and the initialization of the clusters, then, it is necessary to apply a validity index to measure the compactness and the separability of the final clusters and run the clustering algorithm several times. We propose a new fuzzy C-means algorithm in which a validity index based on the concepts of maximum fuzzy energy and minimum fuzzy e

APA, Harvard, Vancouver, ISO, and other styles

24

Hou, Maowen. "Image Processing Based on Fuzzy Mathematics Theory." Security and Communication Networks 2022 (August 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/5287025.

Full text

Abstract:

In order to further improve the problems of poor rationality and weak antinoise ability of existing image processing algorithms and technical algorithms, an image processing research method based on fuzzy mathematical theory is proposed. First, aiming at the ill-posed problem of the PFCM algorithm, the neutrality and rejection degree are used to construct a regular term and embed the algorithm objective function to enhance the correlation between the attribute parameters of the fuzzy set of the sample graph, so as to solve the ill-posed problem of the PFCM algorithm. Secondly, in view of the s

APA, Harvard, Vancouver, ISO, and other styles

25

Beg, Ismat, and Tabasam Rashid. "Fuzzy Distance Measure and Fuzzy Clustering Algorithm." Journal of Interdisciplinary Mathematics 18, no. 5 (2015): 471–92. http://dx.doi.org/10.1080/09720502.2013.842049.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Sinh, Mai Dinh, Le Hung Trinh, and Ngo Thanh Long. "COMBINING FUZZY PROBABILITY AND FUZZY CLUSTERING FOR MULTISPECTRAL SATELLITE IMAGERY CLASSIFICATION." Vietnam Journal of Science and Technology 54, no. 3 (2016): 300. http://dx.doi.org/10.15625/0866-708x/54/3/6463.

Full text

Abstract:

This paper proposes a method of combining fuzzy probability and fuzzy clustering algorithm to classify on multispectral satellite images by relying on fuzzy probability to calculate the number of clusters and the centroid of clusters then using fuzzy clustering to classifying land-cover on the satellite image. In fact, the classification algorithms, the initialization of the clusters and the initial centroid of clusters have great influence on the stability of the algorithms, dealing time and classification results; the unsupervised classification algorithms such as k-Means, c-Means, Iso-data

APA, Harvard, Vancouver, ISO, and other styles

27

Tang, Yiming, Rui Chen, and Bowen Xia. "VSFCM: A Novel Viewpoint-Driven Subspace Fuzzy C-Means Algorithm." Applied Sciences 13, no. 10 (2023): 6342. http://dx.doi.org/10.3390/app13106342.

Full text

Abstract:

Nowadays, most fuzzy clustering algorithms are sensitive to the initialization results of clustering algorithms and have a weak ability to handle high-dimensional data. To solve these problems, we developed the viewpoint-driven subspace fuzzy c-means (VSFCM) algorithm. Firstly, we propose a new cut-off distance. Based on this, we establish the cut-off distance-induced clustering initialization (CDCI) method and use it as a new strategy for cluster center initialization and viewpoint selection. Secondly, by taking the viewpoint obtained by CDCI as the entry point of knowledge, a new fuzzy clust

APA, Harvard, Vancouver, ISO, and other styles

28

DIMITRIADOU, EVGENIA, ANDREAS WEINGESSEL, and KURT HORNIK. "A COMBINATION SCHEME FOR FUZZY CLUSTERING." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 07 (2002): 901–12. http://dx.doi.org/10.1142/s0218001402002052.

Full text

Abstract:

In this paper we present a voting scheme for fuzzy cluster algorithms. This voting method allows us to combine several runs of cluster algorithms resulting in a common partition. This helps us to tackle the problem of choosing the appropriate clustering method for a data set where we have no a priori information about it. We mathematically derive the algorithm from theoretical considerations. Experiments show that the voting algorithm finds structurally stable results. Several cluster validity indexes show the improvement of the voting result in comparison to simple fuzzy voting.

APA, Harvard, Vancouver, ISO, and other styles

29

Cao, Lin, Yunxiao Liu, Dongfeng Wang, Tao Wang, and Chong Fu. "A Novel Density Peak Fuzzy Clustering Algorithm for Moving Vehicles Using Traffic Radar." Electronics 9, no. 1 (2019): 46. http://dx.doi.org/10.3390/electronics9010046.

Full text

Abstract:

The detection of adjacent vehicles in highway scenes has the problem of inaccurate clustering results. In order to solve this problem, this paper proposes a new clustering algorithm, namely Spindle-based Density Peak Fuzzy Clustering (SDPFC) algorithm. Its main feature is to use the density peak clustering algorithm to perform initial clustering to obtain the number of clusters and the cluster center of each cluster. The final clustering result is obtained by a fuzzy clustering algorithm based on the spindle update. The experimental data are the radar echo signal collected in the real highway

APA, Harvard, Vancouver, ISO, and other styles

30

Liu, Yongli, Jingli Chen, and Hao Chao. "A Fuzzy Co-Clustering Algorithm via Modularity Maximization." Mathematical Problems in Engineering 2018 (October 29, 2018): 1–11. http://dx.doi.org/10.1155/2018/3757580.

Full text

Abstract:

In this paper we propose a fuzzy co-clustering algorithm via modularity maximization, named MMFCC. In its objective function, we use the modularity measure as the criterion for co-clustering object-feature matrices. After converting into a constrained optimization problem, it is solved by an iterative alternative optimization procedure via modularity maximization. This algorithm offers some advantages such as directly producing a block diagonal matrix and interpretable description of resulting co-clusters, automatically determining the appropriate number of final co-clusters. The experimental

APA, Harvard, Vancouver, ISO, and other styles

31

He, Si, Nabil Belacel, Alan Chan, Habib Hamam, and Yassine Bouslimani. "A Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Automatic Identification of Clusters." International Journal of Information Technology & Decision Making 15, no. 05 (2016): 949–74. http://dx.doi.org/10.1142/s0219622016500267.

Full text

Abstract:

This paper introduces an alternative fuzzy clustering method that does not require fixing the number of clusters a priori and produce reliable clustering results. This newly proposed method empowers the existing Improved Artificial Fish Swarm algorithm (IAFSA) by the simulated annealing (SA) algorithm. The hybrid approach can prevent IAFSA from unexpected vibration and accelerate convergence rate in the late stage of evolution. Computer simulations are performed to compare this new method with well-known fuzzy clustering algorithms using several synthetic and real-life datasets. Our experiment

APA, Harvard, Vancouver, ISO, and other styles

32

Du, Xinzhi. "A Robust and High-Dimensional Clustering Algorithm Based on Feature Weight and Entropy." Entropy 25, no. 3 (2023): 510. http://dx.doi.org/10.3390/e25030510.

Full text

Abstract:

Since the Fuzzy C-Means algorithm is incapable of considering the influence of different features and exponential constraints on high-dimensional and complex data, a fuzzy clustering algorithm based on non-Euclidean distance combining feature weights and entropy weights is proposed. The proposed algorithm is based on the Fuzzy C-Means soft clustering algorithm to deal with high-dimensional and complex data. The objective function of the new algorithm is modified with the help of two different entropy terms and a non-Euclidean way of computing the distance. The distance calculation formula enha

APA, Harvard, Vancouver, ISO, and other styles

33

Chaudhuri, Arindam. "Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms." Advances in Fuzzy Systems 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/238237.

Full text

Abstract:

Intuitionistic fuzzy sets (IFSs) provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM) algorithm to cluster IFSs by hybridizing concepts of FPCM, IFSs, and distance measures. IFPCM resolves inherent problems encountered with information regarding membership values of objects to each cluster by generalizing membership and nonmembership with hesitancy degree. The algorithm is extended for clustering interval valued intuitionistic

APA, Harvard, Vancouver, ISO, and other styles

34

Xu, Weijun. "Adaptive Fuzzy Kernel Clustering Algorithm." International Journal of Fuzzy Logic Systems 5, no. 4 (2015): 51–58. http://dx.doi.org/10.5121/ijfls.2015.5405.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Yih, Fong-Jhu, Yuan-Horng Lin, and Jeng-Ming Yih. "Clustering with fuzzy supervised algorithm." MATEC Web of Conferences 119 (2017): 01007. http://dx.doi.org/10.1051/matecconf/201711901007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Pratiwi, N. B. I., and D. R. S. Saputro. "Fuzzy c-shells clustering algorithm." Journal of Physics: Conference Series 1613 (August 2020): 012006. http://dx.doi.org/10.1088/1742-6596/1613/1/012006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Amirjavid, Farzad, Sasan Barak, and Hamidreza Nemati. "A Fuzzy Paradigmatic Clustering Algorithm." IFAC-PapersOnLine 52, no. 13 (2019): 2360–65. http://dx.doi.org/10.1016/j.ifacol.2019.11.559.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

JURIO, ARANZAZU, MIGUEL PAGOLA, and HUMBERTO BUSTINCE. "IGNORANCE-BASED FUZZY CLUSTERING ALGORITHM." International Journal of Computational Intelligence and Applications 09, no. 03 (2010): 225–39. http://dx.doi.org/10.1142/s1469026810002884.

Full text

Abstract:

In this work an ignorance-based fuzzy clustering algorithm is presented. The algorithm is based on the entropy-based clustering algorithm proposed by Yao et al.[14]In our proposal, we calculate the total ignorance instead of using the entropy at each data point to select the cluster centers. The experimental results show that the ignorance-based clustering improves the data classification made by the EFC in image segmentation.

APA, Harvard, Vancouver, ISO, and other styles

39

Zhu, Shiyuan, Yuwei Zhao, and Shihong Yue. "Double-Constraint Fuzzy Clustering Algorithm." Applied Sciences 14, no. 4 (2024): 1649. http://dx.doi.org/10.3390/app14041649.

Full text

Abstract:

Given a set of data objects, the fuzzy c-means (FCM) partitional clustering algorithm is favored due to easy implementation, rapid response, and feasible optimization. However, FCM fails to reflect either the importance degree of the individual data objects or that of the clusters. Numerous variants of FCM have been proposed to address these issues. However, most of them cannot effectively apply the available information on data objects or clusters. In this paper, a double-constraint fuzzy clustering algorithm is proposed to reflect the importance degrees of both individual data objects and cl

APA, Harvard, Vancouver, ISO, and other styles

40

Liu, Zhou-zhou, and Shi-ning Li. "WSNs Compressed Sensing Signal Reconstruction Based on Improved Kernel Fuzzy Clustering and Discrete Differential Evolution Algorithm." Journal of Sensors 2019 (June 16, 2019): 1–9. http://dx.doi.org/10.1155/2019/7039510.

Full text

Abstract:

To reconstruct compressed sensing (CS) signal fast and accurately, this paper proposes an improved discrete differential evolution (IDDE) algorithm based on fuzzy clustering for CS reconstruction. Aiming to overcome the shortcomings of traditional CS reconstruction algorithm, such as heavy dependence on sparsity and low precision of reconstruction, a discrete differential evolution (DDE) algorithm based on improved kernel fuzzy clustering is designed. In this algorithm, fuzzy clustering algorithm is used to analyze the evolutionary population, which improves the pertinence and scientificity of

APA, Harvard, Vancouver, ISO, and other styles

41

Li, Kai, Yu Fei Zhou, and Xiao Juan Li. "A Semi-Supervised Fuzzy Clustering Algorithm Based on Mahalanobis Distance and Gaussian Kernel." Applied Mechanics and Materials 385-386 (August 2013): 1513–16. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1513.

Full text

Abstract:

Semi-supervised clustering is a method which can improve clustering performance by introducing partial supervised information. This paper mainly studies the semi-supervised fuzzy clustering which introduces Mahalanobis distance and Gaussian Kernel. And we obtain a new semi-supervised fuzzy clustering objective function. By solving the optimization problem, we propose a semi-supervised fuzzy clustering algorithm F-SCAPC which includes F(M)-SCAPC and F(K)-SCAPC. And we do experimental research for proposed algorithm F-SCAPC using the selected standard data set and the artificial data set. Beside

APA, Harvard, Vancouver, ISO, and other styles

42

Hussain, Ishtiaq, Kristina P. Sinaga, and Miin-Shen Yang. "Unsupervised Multiview Fuzzy C-Means Clustering Algorithm." Electronics 12, no. 21 (2023): 4467. http://dx.doi.org/10.3390/electronics12214467.

Full text

Abstract:

The rapid development in information technology makes it easier to collect vast numbers of data through the cloud, internet and other sources of information. Multiview clustering is a significant way for clustering multiview data that may come from multiple ways. The fuzzy c-means (FCM) algorithm for clustering (single-view) datasets was extended to process multiview datasets in the literature, called the multiview FCM (MV-FCM). However, most of the MV-FCM clustering algorithms and their extensions in the literature need prior information about the number of clusters and are also highly influe

APA, Harvard, Vancouver, ISO, and other styles

43

Wang, Zhen Bo, and Bao Zhi Qiu. "Fuzzy C-Means Clustering Algorithm Based on Coefficient of Variation." Advanced Materials Research 998-999 (July 2014): 873–77. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.873.

Full text

Abstract:

To reduce the impact of irrelevant attributes on clustering results, and improve the importance of relevant attributes to clustering, this paper proposes fuzzy C-means clustering algorithm based on coefficient of variation (CV-FCM). In the algorithm, coefficient of variation is used to weigh attributes so as to assign different weights to each attribute in the data set, and the magnitude of weight is used to express the importance of different attributes to clusters. In addition, for the characteristic of fuzzy C-means clustering algorithm that it is susceptible to initial cluster center value

APA, Harvard, Vancouver, ISO, and other styles

44

Chowdhary, Chiranji Lal, and D. P. Acharjya. "Clustering Algorithm in Possibilistic Exponential Fuzzy C-Mean Segmenting Medical Images." Journal of Biomimetics, Biomaterials and Biomedical Engineering 30 (January 2017): 12–23. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.30.12.

Full text

Abstract:

Different fuzzy segmentation methods were used in medical imaging from last two decades for obtaining better accuracy in various approaches like detecting tumours etc. Well-known fuzzy segmentations like fuzzy c-means (FCM) assign data to every cluster but that is not realistic in few circumstances. Our paper proposes a novel possibilistic exponential fuzzy c-means (PEFCM) clustering algorithm for segmenting medical images. This new clustering algorithm technology can maintain the advantages of a possibilistic fuzzy c-means (PFCM) and exponential fuzzy c-mean (EFCM) clustering algorithms to ma

APA, Harvard, Vancouver, ISO, and other styles

45

Huijing Li, Huijing Li. "Fuzzy Evolutionary Algorithm in Construction Project for Valuation." Journal of Electrical Systems 19, no. 4 (2024): 128–43. http://dx.doi.org/10.52783/jes.628.

Full text

Abstract:

Clustering algorithms has emerged as an important data mining technique for pattern recognition, data analysis and dimensionality reduction. Clustering is usually used in all fields to merge the same feature objects into a single group. The clustering method is incorporated with search algorithms to search the dataset from the databases. For large databases, there is a need of good clustering algorithm with high accuracy. Despite its high performance, the existing methods show some limitations. This paper focused on optimize the clustering method with a search structure for large multidimensio

APA, Harvard, Vancouver, ISO, and other styles

46

RAVI, V., MA BIN, and P. RAVI KUMAR. "THRESHOLD ACCEPTING BASED FUZZY CLUSTERING ALGORITHMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14, no. 05 (2006): 617–32. http://dx.doi.org/10.1142/s0218488506004229.

Full text

Abstract:

In this paper, two new fuzzy clustering algorithms are proposed based on the global optimization metaheuristic, Threshold Accepting. Their effectiveness is demonstrated in the case of five well-known medium sized data sets viz. Iris, Wine, Glass, E.Coli and Olive oil and a large data set Thyroid. In terms of the least objective functions value, these algorithms named TAFC-1 (Threshold Accepting based Fuzzy Clustering) and TAFC-2 outperformed the well-known Fuzzy C-Means (FCM) algorithm in the case of 4 data sets and in the remaining two data sets, FCM marginally outperformed the TAFC. Xie-Beni

APA, Harvard, Vancouver, ISO, and other styles

47

Liu, Yongli, Yajun Zhang, and Hao Chao. "Incremental Fuzzy Clustering Based on Feature Reduction." Journal of Electrical and Computer Engineering 2022 (March 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/8566253.

Full text

Abstract:

In the era of big data, more and more datasets are gradually beyond the application scope of traditional clustering algorithms because of their large scale and high dimensions. In order to break through the limitations, incremental mechanism and feature reduction have become two indispensable parts of current clustering algorithms. Combined with single-pass and online incremental strategies, respectively, we propose two incremental fuzzy clustering algorithms based on feature reduction. The first uses the Weighted Feature Reduction Fuzzy C-Means (WFRFCM) clustering algorithm to process each ch

APA, Harvard, Vancouver, ISO, and other styles

48

Chaomurilige. "How KLFCM Works—Convergence and Parameter Analysis for KLFCM Clustering Algorithm." Mathematics 11, no. 10 (2023): 2285. http://dx.doi.org/10.3390/math11102285.

Full text

Abstract:

KLFCM is a clustering algorithm proposed by introducing K-L divergence into FCM, which has been widely used in the field of fuzzy clustering. Although many studies have focused on improving its accuracy and efficiency, little attention has been paid to its convergence properties and parameter selection. Like other fuzzy clustering algorithms, the output of the KLFCM algorithm is also affected by fuzzy parameters. Furthermore, some researchers have noted that the KLFCM algorithm is equivalent to the EM algorithm for Gaussian mixture models when the fuzzifier λ is equal to 2. In practical applic

APA, Harvard, Vancouver, ISO, and other styles

49

Sеdуkh, l. A., and K. N. Makarov. "FUZZY CLUSTERING OF COMPLEX-VALUED DATA." Vesti vysshikh uchebnykh zavedenii Chernozem ya 19, no. 2 (2023): 46–57. http://dx.doi.org/10.53015/18159958_2023_19_2_46.

Full text

Abstract:

The article discusses the most well-known methods of fuzzy clustering of data: the k-means method and the fuzzy algorithm of c-ellipsoids. Algorithms of these methods are implemented in the MathCAD package and an example with complex-valued data is considered.

APA, Harvard, Vancouver, ISO, and other styles

50

Cao, Lin, Xinyi Zhang, Tao Wang, Kangning Du, and Chong Fu. "An Adaptive Ellipse Distance Density Peak Fuzzy Clustering Algorithm Based on the Multi-target Traffic Radar." Sensors 20, no. 17 (2020): 4920. http://dx.doi.org/10.3390/s20174920.

Full text

Abstract:

In the multi-target traffic radar scene, the clustering accuracy between vehicles with close driving distance is relatively low. In response to this problem, this paper proposes a new clustering algorithm, namely an adaptive ellipse distance density peak fuzzy (AEDDPF) clustering algorithm. Firstly, the Euclidean distance is replaced by adaptive ellipse distance, which can more accurately describe the structure of data obtained by radar measurement vehicles. Secondly, the adaptive exponential function curve is introduced in the decision graph of the fast density peak search algorithm to accura

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!