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Relevant bibliographies by topics / Clustering possibiliste

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Academic literature on the topic 'Clustering possibiliste'

Author: Grafiati

Published: 28 September 2022

Last updated: 25 November 2022

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Journal articles on the topic "Clustering possibiliste"

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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 (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.

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

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Gupta, Saroj Kumar, M. V. Jagannatha Reddy, and A. Nanda Kumar. "Possibilistic Clustering Adaptive Smoothing Bilateral Filter Using Artificial Neural Network." International Journal of Engineering and Technology 2, no. 6 (2010): 499–503. http://dx.doi.org/10.7763/ijet.2010.v2.171.

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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 (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)

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

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

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

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

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ZHOU, JIAN, and CHIH-CHENG HUNG. "A GENERALIZED APPROACH TO POSSIBILISTIC CLUSTERING ALGORITHMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, supp02 (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 theor

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Hamasuna, Yukihiro, Yasunori Endo, and Sadaaki Miyamoto. "On tolerant fuzzy c-means clustering and tolerant possibilistic clustering." Soft Computing 14, no. 5 (2009): 487–94. http://dx.doi.org/10.1007/s00500-009-0451-z.

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Dissertations / Theses on the topic "Clustering possibiliste"

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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 prop

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Holm, Mathias. "Machine learning and spending patterns : A study on the possibility of identifying riskily spending behaviour." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-222016.

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The aim of this study is to research the possibility of using customer transactional data to identify spending patterns among individuals, that in turn can be used to assess creditworthiness. Two different approaches to unsupervised clustering are used and compared in the study, one being K-means and the other an hierarchical approach. The features used in both clustering techniques are extracted from customer transactional data collected from the customers banks. Internal cluster validity indices and credit scores, calculated by credit institutes, are used to evaluate the results of the clust

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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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博士<br>中原大學<br>應用數學研究所<br>98<br>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 initialization

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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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碩士<br>國立交通大學<br>多媒體工程研究所<br>98<br>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 PC

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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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碩士<br>國立臺灣科技大學<br>工業管理系<br>107<br>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 th

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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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碩士<br>國立虎尾科技大學<br>光電與材料科技研究所<br>100<br>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 algorit

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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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碩士<br>國立交通大學<br>多媒體工程研究所<br>104<br>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

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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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Books on the topic "Clustering possibiliste"

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

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

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

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Book chapters on the topic "Clustering possibiliste"

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Wang, Shitong, Fu-lai Chung, Min Xu, Dewen Hu, and Lin Qing. "Possibility Theoretic Clustering." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11538059_88.

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

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

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

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

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

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

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

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

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

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Conference papers on the topic "Clustering possibiliste"

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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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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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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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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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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. 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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Geweniger, Tina, and Thomas Villmann. "Relational and median variants of Possibilistic Fuzzy C-Means." In 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM). IEEE, 2017. http://dx.doi.org/10.1109/wsom.2017.8020032.

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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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Reports on the topic "Clustering possibiliste"

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Cordeiro de Amorim, Renato. A survey on feature weighting based K-Means algorithms. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.79.

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In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algo

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