Готові списки джерел за темами / Possibilistic clustering

Добірка наукової літератури з теми "Possibilistic clustering"

Автор: Grafiati
Опубліковано: 28 вересня 2022

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Зміст

  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій

Статті в журналах з теми "Possibilistic clustering":

1

Miyamoto, Sadaaki, Youhei Kuroda, and Kenta Arai. "Algorithms for Sequential Extraction of Clusters by Possibilistic Method and Comparison with Mountain Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 5 (September 20, 2008): 448–53. http://dx.doi.org/10.20965/jaciii.2008.p0448.

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In addition to fuzzy c-means, possibilistic clustering is useful because it is robust against noise in data. The generated clusters are, however, strongly dependent on an initial value. We propose a family of algorithms for sequentially generating clusters “one cluster at a time,” which includes possibilistic medoid clustering. These algorithms automatically determine the number of clusters. Due to possibilistic clustering's similarity to the mountain clustering by Yager and Filev, we compare their formulation and performance in numerical examples.
2

Yang, Miin-Shen, and Kuo-Lung Wu. "Unsupervised possibilistic clustering." Pattern Recognition 39, no. 1 (January 2006): 5–21. http://dx.doi.org/10.1016/j.patcog.2005.07.005.

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3

Ubukata, Seiki, Katsuya Koike, Akira Notsu, and Katsuhiro Honda. "MMMs-Induced Possibilistic Fuzzy Co-Clustering and its Characteristics." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 5 (September 20, 2018): 747–58. http://dx.doi.org/10.20965/jaciii.2018.p0747.

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In the field of cluster analysis, fuzzy theory including the concept of fuzzy sets has been actively utilized to realize flexible and robust clustering methods. FuzzyC-means (FCM), which is the most representative fuzzy clustering method, has been extended to achieve more robust clustering. For example, noise FCM (NFCM) performs noise rejection by introducing a noise cluster that absorbs noise objects and possibilisticC-means (PCM) performs the independent extraction of possibilistic clusters by introducing cluster-wise noise clusters. Similarly, in the field of co-clustering, fuzzy co-clustering induced by multinomial mixture models (FCCMM) was proposed and extended to noise FCCMM (NFCCMM) in an analogous fashion to the NFCM. Ubukata et al. have proposed noise clustering-based possibilistic co-clustering induced by multinomial mixture models (NPCCMM) in an analogous fashion to the PCM. In this study, we develop an NPCCMM scheme considering variable cluster volumes and the fuzziness degree of item memberships to investigate the specific aspects of fuzzy nature rather than probabilistic nature in co-clustering tasks. We investigated the characteristics of the proposed NPCCMM by applying it to an artificial data set and conducted document clustering experiments using real-life data sets. As a result, we found that the proposed method can derive more flexible possibilistic partitions than the probabilistic model by adjusting the fuzziness degrees of object and item memberships. The document clustering experiments also indicated the effectiveness of tuning the fuzziness degree of object and item memberships, and the optimization of cluster volumes to improve classification performance.
4

ZHOU, JIAN, and CHIH-CHENG HUNG. "A GENERALIZED APPROACH TO POSSIBILISTIC CLUSTERING ALGORITHMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, supp02 (April 2007): 117–38. http://dx.doi.org/10.1142/s0218488507004650.

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Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has advantages over traditional clustering in many applications. Many fuzzy clustering algorithms have been developed in the literature including fuzzy c-means and possibilistic clustering algorithms, which are all objective-function based methods. Different from the existing fuzzy clustering approaches, in this paper, a general approach of fuzzy clustering is initiated from a new point of view, in which the memberships are estimated directly according to the data information using the fuzzy set theory, and the cluster centers are updated via a performance index. This new method is then used to develop a generalized approach of possibilistic clustering to obtain an infinite family of generalized possibilistic clustering algorithms. We also point out that the existing possibilistic clustering algorithms are members of this family. Following that, some specific possibilistic clustering algorithms in the new family are demonstrated by real data experiments, and the results show that these new proposed algorithms are efficient for clustering and easy for computer implementation.
5

De Cáceres, Miquel, Francesc Oliva, and Xavier Font. "On relational possibilistic clustering." Pattern Recognition 39, no. 11 (November 2006): 2010–24. http://dx.doi.org/10.1016/j.patcog.2006.04.008.

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6

Treerattanapitak, Kiatichai, and Chuleerat Jaruskulchai. "Possibilistic Exponential Fuzzy Clustering." Journal of Computer Science and Technology 28, no. 2 (March 2013): 311–21. http://dx.doi.org/10.1007/s11390-013-1331-7.

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7

Pimentel, Bruno Almeida, and Renata M. C. R. de Souza. "A Generalized Multivariate Approach for Possibilistic Fuzzy C-Means Clustering." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, no. 06 (November 27, 2018): 893–916. http://dx.doi.org/10.1142/s021848851850040x.

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Fuzzy c-Means (FCM) and Possibilistic c-Means (PCM) are the most popular algorithms of the fuzzy and possibilistic clustering approaches, respectively. A hybridization of these methods, called Possibilistic Fuzzy c-Means (PFCM), solves noise sensitivity defect of FCM and overcomes the coincident clusters problem of PCM. Although PFCM have shown good performance in cluster detection, it does not consider that different variables can produce different membership and possibility degrees and this can improve the clustering quality as it has been performed with the Multivariate Fuzzy c-Means (MFCM). Here, this work presents a generalized multivariate approach for possibilistic fuzzy c-means clustering. This approach gives a general form for the clustering criterion of the possibilistic fuzzy clustering with membership and possibility degrees different by cluster and variable and a weighted squared Euclidean distance in order to take into account the shape of clusters. Six multivariate clustering models (special cases) can be derivative from this general form and their properties are presented. Experiments with real and synthetic data sets validate the usefulness of the approach introduced in this paper using the special cases.
8

Krishnapuram, R., and J. M. Keller. "A possibilistic approach to clustering." IEEE Transactions on Fuzzy Systems 1, no. 2 (May 1993): 98–110. http://dx.doi.org/10.1109/91.227387.

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9

Xenaki, Spyridoula D., Konstantinos D. Koutroumbas, and Athanasios A. Rontogiannis. "Sparsity-Aware Possibilistic Clustering Algorithms." IEEE Transactions on Fuzzy Systems 24, no. 6 (December 2016): 1611–26. http://dx.doi.org/10.1109/tfuzz.2016.2543752.

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10

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.

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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 maximize benefits and reduce noise/outlier influences. In our proposed hybrid possibilistic exponential fuzzy c-mean segmentation approach, exponential FCM intention functions are recalculated and that select data into the clusters. Traditional FCM clustering process cannot handle noise and outliers so we require being added in clusters due to the reasons of common probabilistic constraints which give the total of membership’s degree in every cluster to be 1. We revise possibilistic exponential fuzzy clustering (PEFCM) which hybridize possibilistic method over exponential fuzzy c-mean segmentation and this proposed idea partition the data filters noisy data or detects them as outliers. Our result analysis by PEFCM segmentation attains an average accuracy of 97.4% compared with existing algorithms. It was concluded that the possibilistic exponential fuzzy c-means segmentation algorithm endorsed for additional efficient for accurate detection of breast tumours to assist for the early detection.
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Статті в журналах

Дисертації з теми "Possibilistic clustering":

1

Ben, marzouka Wissal. "Traitement possibiliste d'images, application au recalage d'images." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2022. http://www.theses.fr/2022IMTA0271.

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Dans ce travail, nous proposons un système de recalage géométrique possibiliste qui fusionne les connaissances sémantiques et les connaissances au niveau du gris des images à recaler. Les méthodes de recalage géométrique existantes se reposent sur une analyse des connaissances au niveau des capteurs lors de la détection des primitives ainsi que lors de la mise en correspondance. L'évaluation des résultats de ces méthodes de recalage géométrique présente des limites au niveau de la perfection de la précision causées par le nombre important de faux amers. L’idée principale de notre approche proposée est de transformer les deux images à recaler en un ensemble de projections issues des images originales (source et cible). Cet ensemble est composé des images nommées « cartes de possibilité », dont chaque carte comporte un seul contenu et présente une distribution possibiliste d’une classe sémantique des deux images originales. Le système de recalage géométrique basé sur la théorie de possibilités proposé présente deux contextes : un contexte supervisé et un contexte non supervisé. Pour le premier cas de figure nous proposons une méthode de classification supervisée basée sur la théorie des possibilités utilisant les modèles d'apprentissage. Pour le contexte non supervisé, nous proposons une méthode de clustering possibiliste utilisant la méthode FCM-multicentroide. Les deux méthodes proposées fournissent en résultat les ensembles de classes sémantiques des deux images à recaler. Nous créons par la suite, les bases de connaissances pour le système de recalage possibiliste proposé. Nous avons amélioré la qualité du recalage géométrique existant en termes de perfection de précision, de diminution du nombre de faux amers et d'optimisation de la complexité temporelle
In this work, we propose a possibilistic geometric registration system that merges the semantic knowledge and the gray level knowledge of the images to be registered. The existing geometric registration methods are based on an analysis of the knowledge at the level of the sensors during the detection of the primitives as well as during the matching. The evaluation of the results of these geometric registration methods has limits in terms of the perfection of the precision caused by the large number of outliers. The main idea of our proposed approach is to transform the two images to be registered into a set of projections from the original images (source and target). This set is composed of images called “possibility maps”, each map of which has a single content and presents a possibilistic distribution of a semantic class of the two original images. The proposed geometric registration system based on the possibility theory presents two contexts: a supervised context and an unsupervised context. For the first case, we propose a supervised classification method based on the theory of possibilities using learning models. For the unsupervised context, we propose a possibilistic clustering method using the FCM-multicentroid method. The two proposed methods provide as a result the sets of semantic classes of the two images to be registered. We then create the knowledge bases for the proposed possibilistic registration system. We have improved the quality of the existing geometric registration in terms of precision perfection, reductionin the number of false landmarks and optimization of time complexity
2

Lai, Chien-Yo, and 賴建佑. "A Robust Possibilistic Clustering Algorithm." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/45186783961780903545.

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Анотація:
博士
中原大學
應用數學研究所
98
Krishnapuram and Keller (1993) first proposed a possibilistic approach to clustering, called possibilistic c-means (PCM), by relaxing the constraint in fuzzy c-means (FCM) that the memberships of a data point across classes sum to 1. The PCM algorithm has a tendency to produce coincident clusters. This can be a merit of PCM as a good mode-seeking algorithm if initials and parameters are suitably chosen. However, the performance of PCM heavily depends on the selection of parameters and initializations. In this paper, for solving these parameters and initializations selection problems, we propose a new scheme of PCM, called an automatic merging possibilistic clustering method (AM-PCM). The proposed AM-PCM algorithm first uses all data points as initial prototypes and then automatically merges these surrounding points around each cluster mode such that it can self-organize data groups according to the original data structure.
3

Cheng, Yu-Rong, and 鄭俞榮. "Metaheuristic-Based Possibilistic Fuzzy k-modes Algorithms for Categorical Data Clustering." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/fz5xw5.

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Анотація:
碩士
國立臺灣科技大學
工業管理系
107
Recently, smart devices and technology applications are applied widely in many fields. An enormous amount of information is recorded and collected rapidly. Thus, the process to analyze and obtain valuable information from the data becomes a very crucial issue. Clustering analysis plays an important role to solve the aforementioned issue. However, facing with the different types of data, the appropriate approach should be chosen to handle the data. This study focuses on categorical data. A possibilistic fuzzy k-modes (PFKM) algorithm is proposed by combining the possibilistic concept with fuzzy k-modes (FKM) algorithm in order to alleviate the effects of outlier points and improve the clustering result. In addition, this study also implements three metaheuristics, namely genetic algorithm (GA), particle swarm optimization (PSO), and sine-cosine algorithm (SCA) in order to enhance the clustering performance. Therefore, three clustering algorithms are proposed in this study, named GA-PFKM, PSO-PFKM, and SCA-PFKM algorithms. The proposed algorithms are utilized to perform a cluster analysis for eight categorical datasets. The performance of the algorithms is compared with the classical FKM algorithm using two indexes, namely sum-of-squared error (SSE) and accuracy. The experimental results indicate that PSO-PFKM and SCA-PFKM algorithms obtain the better performance for most of the datasets. Furthermore, this study analyzes the clustering result for breast cancer dataset more detailed. The analysis reveals that people with a higher range of normal nucleoli, bare nuclei, and clump thickness have a higher risk of breast cancer.
4

劉強. "Analysis of Shell Clustering Algorithms for Template-Based Shapes that Combine Fuzzy and Possibilistic Clustering Approaches." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/41996598083117357187.

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Анотація:
碩士
國立交通大學
多媒體工程研究所
98
This goal of this thesis is to investigate the results of data clustering. Specifically, we want to study the effect of fuzzy c-means (FCM) and possibilistic c-means (PCM), as well as their combinations, in template-based shell clustering. Template-based shell clustering is the process of detecting clusters of particular geometrical shapes through clustering algorithms. The use of FCM and PCM in shell clustering has appeared in many research. However, both FCM and PCM have their shortcomings. For example, the results of FCM are highly affected by noise, and PCM tends to produce overlapping clusters. We are particularly interested in whether the combination of FCM and PCM algorithms can improve the results of shell clustering. Here we use two combinational algorithms in the literature, possibilistic fuzzy c-means (PFCM) and improved possibilistic c-means (IPCM). Our results indicate that IPCM and PFCM have better shape detection results than FCM and PCM when used with template-based shell clustering of complex or noisy data. We also discover that different combination methods have different properties that are helpful in clustering.
5

Chang, Sheng-Chieh, and 張勝傑. "Rough Interval Possibilistic Fuzzy C-Means Clustering Algorithms and Implemented on Smart Phone." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/e57f8z.

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Анотація:
碩士
國立虎尾科技大學
光電與材料科技研究所
100
Clustering algorithms have been widely used such as pattern recognition, data mining and machine learning, etc. It is an unsupervised classification that is divided into groups according to data sets. That is, the data sets of similarity partition belong to the same group; otherwise data sets divide other groups in the clustering algorithms. In general, clustering methods are divided into partitioning-based, hierarchical, density-based, grid-based and model-based. In this thesis, we focus on the partitioning-based approach. K-means (KM) clustering algorithm is famous hard clustering that also belongs to partitioning-based. It is definitely to partition into group that only belonging to a particular group; however, the partition is not suitable to deal with fuzzy situation. Bezdek firstly proposed an improved KM clustering algorithm; namely, fuzzy c-means (FCM) clustering algorithm. The FCM clustering algorithm applied fuzzy theory concept of which the data sets not belong to specific group but membership have to representation. In the FCM clustering algorithm is difficult to deal with data sets with noise and outliers. Therefore, the many papers proposed many approaches; namely, possiblilistic c-means (PCM) clustering algorithm, fuzzy possiblilistic c-means (FPCM) clustering algorithm and possiblilistic fuzzy c-means (PFCM) clustering algorithm to overcome this problem. On the other hand, the interval FCM (IFCM) clustering method was proposed to deal with symbolic interval data. However, it still has noisy and outliers problems. Hence, we propose interval PCM (IPCM) clustering algorithm, interval FPCM (IFPCM) clustering algorithm and interval PFCM (IPFCM) clustering algorithm to overcome the IFCM clustering algorithm for the symbolic interval data clustering in noisy and outlier environments. In order to efficient handling of overlapping partitions problem the rough set based generalized FCM algorithm was proposed. This approach includes rough set and fuzzy set of which the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in class definition. Therefore, we consider advantage of rough set. Hence, we combine the rough set with our propose algorithm for application. That is, we proposed rough IPCM (RIPCM) clustering algorithm, rough IFPCM (RIFPCM) clustering algorithm and rough IPFCM (RIPFCM) clustering algorithm that can to efficient handling of overlapping partitions problem for symbolic interval data. Finally, we also implement the proposed algorithms to smart phone.
6

Yang, Tzu-Chieh, and 楊子頡. "Three-Dimensional Possibilistic C-Template Shell Clustering and its Application in 3D Object Segmentation." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/38716084468398410151.

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Анотація:
碩士
國立交通大學
多媒體工程研究所
104
The purpose of this thesis is to use a model to match a similar object in three-dimensional space.This research includes four main parts: First, using the Kinect sensor to take the real world; second, splitting the point cloud into separate items; third, creating a model to match each individual item; lastly, getting the final result. The thesis includes descriptions on using Kinect to establish a point cloud, using 3D Hough Transform to find and remove the cloud points of planes, and using connected-component to separate individual objects. The focus of this thesis is on matching with individual item and manually created models through the Template-Based Shell Clustering that is the process of detecting clusters of particular geometrical shapes through clustering algorithms. In experimental results, we can see accurate matching results.
7

Ghosh, Debashis. "A Possibilistic Approach To Handwritten Script Identification Via Morphological Methods For Pattern Representation." Thesis, 1999. http://etd.iisc.ernet.in/handle/2005/1673.

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Книги з теми "Possibilistic clustering":

1

Viattchenin, Dmitri A. A heuristic approach to possibilistic clustering: Algorithms and applications. Heidelberg: Springer, 2013.

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2

Viattchenin, Dmitri A. A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35536-3.

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3

Viattchenin, Dmitri A. A. A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications. Springer, 2015.

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Частини книг з теми "Possibilistic clustering":

1

Ferone, Alessio, and Antonio Maratea. "Graded Possibilistic Meta Clustering." In Neural Approaches to Dynamics of Signal Exchanges, 189–99. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8950-4_18.

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2

Viattchenin, Dmitri A. "Heuristic Algorithms of Possibilistic Clustering." In A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications, 59–118. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35536-3_2.

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3

Xenaki, Spyridoula D., Konstantinos D. Koutroumbas, and Athanasios A. Rontogiannis. "Sequential Sparse Adaptive Possibilistic Clustering." In Artificial Intelligence: Methods and Applications, 29–42. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07064-3_3.

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4

Ammar, Asma, and Zied Elouedi. "A New Possibilistic Clustering Method: The Possibilistic K-Modes." In AI*IA 2011: Artificial Intelligence Around Man and Beyond, 413–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23954-0_40.

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5

Viattchenin, Dmitri A. "Applications of Heuristic Algorithms of Possibilistic Clustering." In A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications, 183–218. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35536-3_4.

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6

Ammar, Asma, Zied Elouedi, and Pawan Lingras. "K-Modes Clustering Using Possibilistic Membership." In Communications in Computer and Information Science, 596–605. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31718-7_61.

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7

Viattchenin, Dmitri A. "Clustering Approaches for Uncertain Data." In A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications, 119–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35536-3_3.

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8

Szilágyi, László. "Robust Clustering Algorithms Employing Fuzzy-Possibilistic Product Partition." In Fuzzy Sets, Rough Sets, Multisets and Clustering, 101–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47557-8_7.

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9

Zhou, Jie, Can Gao, and Jia Yin. "Rough Possibilistic Clustering for Fabric Image Segmentation." In Artificial Intelligence on Fashion and Textiles, 247–53. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99695-0_30.

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10

Yu, Hong, and Hu Luo. "A Novel Possibilistic Fuzzy Leader Clustering Algorithm." In Lecture Notes in Computer Science, 423–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10646-0_51.

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Тези доповідей конференцій з теми "Possibilistic clustering":

1

Xenaki, Spyridoula D., Konstantinos D. Koutroumbas, and Athanasios A. Rontogiannis. "Adaptive possibilistic clustering." In 2013 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 2013. http://dx.doi.org/10.1109/isspit.2013.6781918.

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2

Xenaki, Spyridoula D., Konstantinos D. Koutroumbas, and Athanasios A. Rontogiannis. "Sparse adaptive possibilistic clustering." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854165.

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3

Antoine, Violaine, Jose A. Guerrero, Tanya Boone, and Gerardo Romero. "Possibilistic clustering with seeds." In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491655.

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Runkler, Thomas A., and James M. Keller. "Sequential possibilistic one-means clustering." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015413.

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Koutsibella, Aggeliki, and Konstantinos D. Koutroumbas. "Stochastic gradient descent possibilistic clustering." In SETN 2020: 11th Hellenic Conference on Artificial Intelligence. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3411408.3411436.

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Lei Wang, Hongbing Ji, and Xinbo Gao. "Fully Unsupervised Possibilistic Entropy Clustering." In 2006 IEEE International Conference on Fuzzy Systems. IEEE, 2006. http://dx.doi.org/10.1109/fuzzy.2006.1682027.

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Jafar, O. A. Mohamed, and R. Sivakumar. "A study on possibilistic and fuzzy possibilistic C-means clustering algorithms for data clustering." In 2012 International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET). IEEE, 2012. http://dx.doi.org/10.1109/incoset.2012.6513887.

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Kanzawa, Yuchi. "On Possibilistic Clustering Algorithms Based on Noise Clustering." In 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS). IEEE, 2016. http://dx.doi.org/10.1109/scis-isis.2016.0023.

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Ruprecht, Blake, Wenlong Wu, Muhammad Aminul Islam, Derek Anderson, James Keller, Grant Scott, Curt Davis, et al. "Possibilistic Clustering Enabled Neuro Fuzzy Logic." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177593.

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Kung, Chung-Chun, Hong-Chi Ku, and Jui-Yiao Su. "Possibilistic c-regression models clustering algorithm." In 2013 IEEE International Conference on System Science and Engineering (ICSSE). IEEE, 2013. http://dx.doi.org/10.1109/icsse.2013.6614679.

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