Academic literature on the topic 'Clustering possibiliste'
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Journal articles on the topic "Clustering possibiliste"
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.
Full textYang, 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.
Full textGupta, 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.
Full textPimentel, 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.
Full textDe 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.
Full textTreerattanapitak, 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.
Full textKrishnapuram, 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.
Full textXenaki, 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.
Full textZHOU, 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.
Full textHamasuna, Yukihiro, Yasunori Endo, and Sadaaki Miyamoto. "On tolerant fuzzy c-means clustering and tolerant possibilistic clustering." Soft Computing 14, no. 5 (June 3, 2009): 487–94. http://dx.doi.org/10.1007/s00500-009-0451-z.
Full textDissertations / Theses on the topic "Clustering possibiliste"
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.
Full textIn 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
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.
Full textMålet med detta arbete är att studera möjligheten att använda data om individers kontotransaktioner för att identifiera utgiftsmönster hos individer, som i sin tur kan användas för att utvärdera kreditvärdighet. Två olika tillvägagångssätt som använder oövervakad klustring (eng. unsupervised clustering) används och utvärderas i rapporten, den ena är K-means och den andra är en hierarkisk teknik. De attribut (eng. features) som används i de båda klustrings teknikerna utvinns från data som innehåller kontotransaktioner och som erhålls från banker. Interna kluster värde index (eng. cluster validity indices) och individers riskprognoser, som beräknats av ett kreditinstitut, används för att utvärdera resultaten från klustrings teknikerna. Vi menar att resultaten som presenteras i denna rapport visar att målet till viss del uppnåtts, men att mer data och forskning krävs. Vidare forskning som föreslås är att lägga till fler attribut (eng. features) till modellerna och utvärdera effekten på de resulterande klusterna.
Lai, Chien-Yo, and 賴建佑. "A Robust Possibilistic Clustering Algorithm." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/45186783961780903545.
Full text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.
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.
Full text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.
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.
Full text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.
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.
Full text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.
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.
Full text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.
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.
Full textBooks on the topic "Clustering possibiliste"
Viattchenin, Dmitri A. A heuristic approach to possibilistic clustering: Algorithms and applications. Heidelberg: Springer, 2013.
Find full textViattchenin, 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.
Full textViattchenin, Dmitri A. A. A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications. Springer, 2015.
Find full textBook chapters on the topic "Clustering possibiliste"
Wang, Shitong, Fu-lai Chung, Min Xu, Dewen Hu, and Lin Qing. "Possibility Theoretic Clustering." In Lecture Notes in Computer Science, 849–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11538059_88.
Full textFerone, 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.
Full textXenaki, 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.
Full textViattchenin, 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.
Full textSzilágyi, 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.
Full textAmmar, 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.
Full textAmmar, 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.
Full textViattchenin, 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.
Full textViattchenin, 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.
Full textZhou, 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.
Full textConference papers on the topic "Clustering possibiliste"
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.
Full textAntoine, 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.
Full textXenaki, 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.
Full textKanzawa, 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.
Full textRunkler, 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.
Full textKoutsibella, 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.
Full textLei 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.
Full textJafar, 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.
Full textGeweniger, 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.
Full textKung, 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.
Full textReports on the topic "Clustering possibiliste"
Cordeiro de Amorim, Renato. A survey on feature weighting based K-Means algorithms. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ser.v1i2.79.
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