Log in

Relevant bibliographies by topics / Clustering possibiliste

Contents

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers
Reports

Academic literature on the topic 'Clustering possibiliste'

Author: Grafiati

Published: 28 September 2022

Last updated: 25 November 2022

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

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Clustering possibiliste.'

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.

Journal articles on the topic "Clustering possibiliste"

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 (2008): 448–53. http://dx.doi.org/10.20965/jaciii.2008.p0448.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Dissertations / Theses on the topic "Clustering possibiliste"

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.

Full text

Abstract:

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<br>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

APA, Harvard, Vancouver, ISO, and other styles

2

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 text

Abstract:

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 clustering techniques. Based on the experiments in this report, we believe that the approach exhibit interesting results and that further research with evaluation on a larger dataset is desired. Proposed future work is to append additional features to the models and study the effect on the resulting clusters.<br>Målet 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.

APA, Harvard, Vancouver, ISO, and other styles

3

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

Full text

Abstract:

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 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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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 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.

APA, Harvard, Vancouver, ISO, and other styles

5

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

Abstract:

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 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.

APA, Harvard, Vancouver, ISO, and other styles

6

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

Abstract:

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 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.

APA, Harvard, Vancouver, ISO, and other styles

7

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

Abstract:

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 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.

APA, Harvard, Vancouver, ISO, and other styles

8

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 text

APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Clustering possibiliste"

1

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Clustering possibiliste"

1

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Clustering possibiliste"

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Clustering possibiliste"

1

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.

Full text

Abstract:

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 algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means

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!