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Rough Hypercuboid Based Generalized and Robust IT2 Fuzzy c-means algorithm

IEEE TRANSAcTIONS ON cYBERNETIcS 2021年第7期51卷 3641-3652页

作者： Maji, Pradipta Garai, Partha Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata 700108 India Kalyani Govt Engn Coll Dept Comp Applicat Kalyani 741235 W Bengal India

One of the important issues in pattern recognition and machine learning is how to find natural groups present in a dataset. In this regard, this paper presents a novel clustering algorithm, called rough hypercuboid-based interval type-2 fuzzy c-means (RIT2FcM). It judiciously integrates the merits of the rough hypercuboid approach, c-means algorithm, and interval type-2 fuzzy set, to address the uncertainty associated with real-life datasets. Using the concept of hypercuboid equivalence partition matrix (HEM) of rough hypercuboid approach, the lower approximation and boundary region of each cluster are implicitly defined, without using any prespecified threshold parameter. The interval-valued fuzzifier is applied to address the uncertainty coupled with different parameters of rough-fuzzy clustering algorithms, where the determination of the appropriate value of fuzzifier is a difficult task. An analytical formulation on the convergence analysis of the proposed RIT2FcM algorithm, along with a theoretical bound of its fuzzifier, is also introduced. The efficacy of the proposed RIT2FcM method is extensively compared with that of several existing clustering algorithms, using some cluster validity and classification rate indices on various real-life datasets. The proposed algorithm performs better than the state-of-the-art c-means algorithms in 92.59% cases, with respect to different cluster validity indices, in lesser computation time.

关键词： c-means algorithm clustering interval type-2 fuzzy set pattern recognition rough sets

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Application of Image Segmenting Technology Based on Fuzzy c-means algorithm in competition Video Referee

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IEEE AccESS 2024年 12卷 34378-34389页

作者： Yan, Xu North China Elect Power Univ Dept Phys Educ Baoding 071003 Peoples R China

As sports developing and popularizing, people have put forward increasing demands for fairness and transparency in competitions. However, traditional competition referee systems rely too heavily on human subjective judgment, which often leads to significant subjectivity and in-accuracy issues. To overcome these difficulties, some improvements have been made on the basis of the fuzzy c-means algorithm to preserve more information, while also suppressing membership to improve the convergence speed of the algorithm. These results confirm that the accuracy of the model converged approximately in the 600th round, with an accuracy of 96.1%. compared with the standard fuzzy c-means algorithm, the convergence speed has been improved by 33% and the accuracy has been improved by 2.1%. This experiment verifies the effectiveness and practicality of the video referee in this competition. This provides new ideas for the development of image segmenting technology and has certain application potential in sports competition refereeing, judicial notarization refereeing, and other fields.

关键词： Image segmentation Fuzzy sets clustering algorithms Mathematical models Object segmentation Fuzzy logic Biomedical imaging Visual analytics Fuzzy theory c-means algorithm image segmenting technology video image recognition video referee

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A New Method for Bad Data Identification of Oilfield System Based on Enhanced Gravitational Search-Fuzzy c-means algorithm

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IEEE TRANSAcTIONS ON INDUSTRIAL INFORMATIcS 2019年第11期15卷 5963-5970页

作者： Zhao, Yang Xu, Jianjun Wu, Jingchun Northeast Petr Univ Key Lab Enhanced Oil Recovery Minist Educ Daqing 163318 Peoples R China Northeast Petr Univ Heilongjiang Prov Key Lab Networking & Intelligen Daqing 163318 Peoples R China

Aiming at the shortcomings of current methods of bad data detection and identification in a power system, this article advances a new method to identify bad data of the power system based on the enhanced gravitation search-fuzzy c-mean algorithm (EGSA-FcM). By using the enhanced gravity search algorithm (EGSA), a better initial solution to search the measurement data uploaded by ScADA system is obtained. Then, the FcM algorithm was used to obtain the classification of benign data and bad data. Finally, through the cOS clustering validity judgment index, the optimal clustering number is determined and the optimal clustering results and bad data were obtained. This method has already been applied to the IEEE 14-node power system and a regional power grid in Daqing, china. The results indicated that the proposed method is more accurate than the traditional ones and effectively avoid the occurrence of false detection and missed detection.

关键词： Bad data identification c-means algorithm enhanced gravity search algorithm (EGSA) fuzzy clustering

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cAST-WSN: The Presentation of New clustering algorithm Based on Steiner Tree and c-means algorithm Improvement in Wireless Sensor Networks

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WIRELESS PERSONAL cOMMUNIcATIONS 2017年第1期97卷 1323-1344页

作者： Baradaran, Amir Abbas Navi, Keivan Shahid Beheshti Univ Dept Elect & Comp Engn Tehran Iran Shahid Beheshti Univ Fac Elect & Comp Engn Tehran Iran

In recent years, both energy consumption and network lifetime have been among the most important challenges in wireless sensor networks. Due to the limited energy of sensor nodes and the inability sensors recharge, many clustering-based methods have been offered to transmit the received information to the base station by sensors. In this paper, a new method called cAST-WSN has been presented, which is based on three criteria: the node distance from the gravity center of the cluster, the node distance from the gravity center of cluster nodes and the node distance from the energy center of nodes in each cluster. Based on these distances and Steiner tree structure, there exist two types of clustering. In this research, we present a function to evaluate the quality of cluster by means of which the quality of clusters in the two presented states can be examined and the best type of clustering can be selected. cAST-WSN method has been successful in bringing significant improvements to c-means clustering algorithm. The results from simulation indicate that the clustering based on cAST-WSN method, as compared with the previous similar methods such as LEAcH, LEAcH-c, LEAcH-Mobile, ALEAcH, LEAcH-SWDN, cBL, PEcRP, HUcL, O-LEAcH and DcH, has improved the network lifetime. Furthermore, cAST-WSN method has improved a main criterion called cluster density. cAST-WSN simulation results show that the clustering process in this method is better than c-means algorithm.

关键词： Wireless sensor network Steiner tree c-means algorithm clustering

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Surface defect recognition using multi-layer perceptron and c-means algorithm

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLIcATIONS AND PRAcTIcE 2001年第1期8卷 52-61页

作者： Kim, T Kumara, SRT Kyungsung Univ Dept Ind Engn Pusan 608736 South Korea Penn State Univ Dept Ind & Mfg Engn University Pk PA 16802 USA

Quality assurance in the powder injection molding is a critical problem due to its complicated processing methods. As surface conditions are major issues for the product quality of the powder injection molding, automated visual inspection on the surface is highly demanded. This paper proposes representation and recognition schemes for the surface defects on the powder injection molding. From the edge image, line segments were extracted, then they were represented using parameters. Multi-layer perceptron and c-means algorithm were tested to recognize defective features in the powder injection molding. The neural network method showed better recognition for the defective features based on the selected measures. Significance: The surface defect in powder injection molding is a critical problem for the product quality assurance. From the complicated surface features, the recognition of defective features were compared between an artificial neural network and traditional pattern recognition method.

关键词： surface defect powder injection molding multi-layer perceptron c-means algorithm pattern recognition

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Agent Based Segmentation of the MRI Brain Using a Robust c-means algorithm

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Journal of computer and communications 2016年第10期4卷 13-21页

作者： Hanane Barrah Abdeljabbar cherkaoui Driss Sarsri Laboratory of Innovative Technologies National School of Applied Sciences Abdelmalek Essadi University Tangier Morocco

In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of the most active research fields in the medical imaging domain. Because of the fuzzy nature of the MRI images, many researchers have adopted the fuzzy clustering approach to segment them. In this work, a fast and robust multi-agent system (MAS) for MRI segmentation of the brain is proposed. This system gets its robustness from a robust c-means algorithm (RFcM) and obtains its fastness from the beneficial properties of agents, such as autonomy, social ability and reactivity. To show the efficiency of the proposed method, we test it on a normal brain brought from the BrainWeb Simulated Brain Database. The experimental results are valuable in both robustness to noise and running times standpoints.

关键词： Agents and MAS MR Images Fuzzy clustering c-means algorithm Image Segmentation

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EcoG signal classification based on HHT and PSO-c-means algorithm

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Wuhan Ligong Daxue Xuebao/Journal of Wuhan University of Technology 2008年第1期30卷 102-105页

作者： Lu, Jun Li, Sheng-Yong School of Electronic and Information Engineering South China University of Technology Guangzhou 510641 China

Electrocorticogram (EcoG) is a kind of non-stationary signal. Feature extraction and classification are crucial for good EcoG based brain-computer interface (EcoG-BcI). In this paper, a method based on Hilbert-Hung transform (HHT) is proposed to extract and compress the features of EcoG narrow bands. Then particle swarm optimization (PSO) and G-means algorithm are wrapped to adjust feature weights and enhance classification result. Only using 6 channels, the test accuracy of 93% is achieved on Data set I of BcI competition III.

关键词： c-means algorithm EcoG based brain-computer interface (EcoG-BcI) Hilbert-Hung transform (HHT) Particle swarm optimization (PSO)

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Metaheuristic-based possibilistic multivariate fuzzy weighted c-means algorithms for market segmentation

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APPLIED SOFT cOMPUTING 2020年 96卷 106639-106639页

作者： Kuo, R. J. Amornnikun, Patipharn Thi Phuong Quyen Nguyen Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sect 4Kee Lung Rd Taipei 106 Taiwan Univ Sci & Technol Univ Danang Fac Project Management 54 Nguyen Luong Bang Danang Vietnam

This study proposed the metaheuristic-based possibilistic multivariate fuzzy weighted c-means (PMFWcM) algorithm for clustering mixed data PMFWcM algorithm itself is normally used for numerical data. To implement in the real application of market segmentation, where the data usually contains both numerical and categorical attributes model improvement is a need. First, the distance between two mixed-attribute objects is calculated by using the object-cluster similarity measure. Then, three metaheuristics, i.e., genetic algorithm (GA), particle swarm optimization algorithm (PSO), and sine cosine algorithm (ScA), are employed to integrate with the PMFWcM algorithm for cluster analysis. This combination aims to improve the clustering performance of the PMFWcM algorithm and to make the clustering results more stable. To cluster a real-world dataset certainly, the experiment with benchmark datasets from UcI machine learning repository is conducted to verify the performance of the proposed algorithms. The experiment results show that the clustering performance of the ScA-PMFWcM, GA-PMFWcM and PSO-PMFWcM algorithms are better than that of the PMFWcM algorithm. Moreover, from case study results, the ScA-PMFWcM algorithm gives the smallest sum of squared error and computational time compared with the GA-PMFWcM, PSO-PMFWcM, and PMFWcM algorithms. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Possibilistic multivariate fuzzy weighted c-means algorithm Mixed data Market segmentation Metaheuristics Sine cosine algorithm

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Shadowed c-means: Integrating fuzzy and rough clustering

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PATTERN REcOGNITION 2010年第4期43卷 1282-1291页

作者： Mitra, Sushmita Pedrycz, Witold Barman, Bishal Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2G7 Canada SV Natl Inst Technol Dept Elect Engn Surat 395007 India

A new method of partitive clustering is developed in the framework of shadowed sets. The core and exclusion regions of the generated shadowed partitions result in a reduction in computations as compared to conventional fuzzy clustering. Unlike rough clustering, here the choice of threshold parameter is fully automated. The number of clusters is optimized in terms of various validity indices. It is observed that shadowed clustering can efficiently handle overlapping among clusters as well as model uncertainty in class boundaries. The algorithm is robust in the presence of outliers. A comparative study is made with related partitive approaches. Experimental results on synthetic as well as real data sets demonstrate the superiority of the proposed approach. (c) 2009 Elsevier Ltd. All rights reserved.

关键词： Shadowed sets c-means algorithm Three-valued logic cluster validity index Fuzzy sets Rough sets

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A NEW NONITERATIVE APPROAcH FOR cLUSTERING

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PATTERN REcOGNITION LETTERS 1994年第2期15卷 125-133页

作者： YIN, PY cHEN, LH NATL CHIAO TUNG UNIV DEPT COMP & INFORMAT SCIHSINCHU 30050TAIWAN

In this paper, a new non-iterative clustering method is proposed. It consists of two passes. In the first pass, the mean distance from one object to its nearest neighbor is estimated. Based on this distance, those noises far away from objects are extracted and removed. In the second pass, the mean distance from the remaining objects to their nearest neighbors is computed. Based on the distance, all the intrinsic clusters are then found. The proposed method is non-iterative and can automatically determine the number of clusters. Experimental results also show that the partition generated by the proposed method is more reasonable than that of the well-known c-means algorithm in many complicated object distributions.

关键词： cLUSTERING c-means algorithm FIXED-RADIUS MEAN MINIMUM DISTANcE NOISE cLUSTER

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