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Статті в журналах з теми "Co-clustering algorithm"
Kanzawa, Yuchi. "Bezdek-Type Fuzzified Co-Clustering Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (November 20, 2015): 852–60. http://dx.doi.org/10.20965/jaciii.2015.p0852.
Повний текст джерелаLiu, Yongli, Jingli Chen, and Hao Chao. "A Fuzzy Co-Clustering Algorithm via Modularity Maximization." Mathematical Problems in Engineering 2018 (October 29, 2018): 1–11. http://dx.doi.org/10.1155/2018/3757580.
Повний текст джерелаZhang, Yinghui. "A Kernel Probabilistic Model for Semi-supervised Co-clustering Ensemble." Journal of Intelligent Systems 29, no. 1 (December 30, 2017): 143–53. http://dx.doi.org/10.1515/jisys-2017-0513.
Повний текст джерелаGu, Yi, and Kang Li. "Entropy-Based Multiview Data Clustering Analysis in the Era of Industry 4.0." Wireless Communications and Mobile Computing 2021 (April 30, 2021): 1–8. http://dx.doi.org/10.1155/2021/9963133.
Повний текст джерелаJin, Chun Xia, Hui Zhang, and Qiu Chan Bai. "Text Clustering Algorithm of Co-Occurrence Word Based on Association-Rule Mining." Applied Mechanics and Materials 599-601 (August 2014): 1749–52. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1749.
Повний текст джерелаHussain, Syed Fawad, and Shahid Iqbal. "CCGA: Co-similarity based Co-clustering using genetic algorithm." Applied Soft Computing 72 (November 2018): 30–42. http://dx.doi.org/10.1016/j.asoc.2018.07.045.
Повний текст джерелаHou, Jie, Xiufen Ye, Chuanlong Li, and Yixing Wang. "K-Module Algorithm: An Additional Step to Improve the Clustering Results of WGCNA Co-Expression Networks." Genes 12, no. 1 (January 12, 2021): 87. http://dx.doi.org/10.3390/genes12010087.
Повний текст джерелаMA, PATRICK C. H., KEITH C. C. CHAN, and DAVID K. Y. CHIU. "CLUSTERING AND RE-CLUSTERING FOR PATTERN DISCOVERY IN GENE EXPRESSION DATA." Journal of Bioinformatics and Computational Biology 03, no. 02 (April 2005): 281–301. http://dx.doi.org/10.1142/s0219720005001053.
Повний текст джерелаShang, Ronghua, Yang Li, and Licheng Jiao. "Co-evolution-based immune clonal algorithm for clustering." Soft Computing 20, no. 4 (February 7, 2015): 1503–19. http://dx.doi.org/10.1007/s00500-015-1602-z.
Повний текст джерелаLiu, Yongli, Shuai Wu, Zhizhong Liu, and Hao Chao. "A fuzzy co-clustering algorithm for biomedical data." PLOS ONE 12, no. 4 (April 26, 2017): e0176536. http://dx.doi.org/10.1371/journal.pone.0176536.
Повний текст джерелаДисертації з теми "Co-clustering algorithm"
Mohd, Yusoh Zeratul Izzah. "Composite SaaS resource management in cloud computing using evolutionary computation." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/63280/1/Zeratul_Mohd_Yusoh_Thesis.pdf.
Повний текст джерелаSchmutz, Amandine. "Contributions à l'analyse de données fonctionnelles multivariées, application à l'étude de la locomotion du cheval de sport." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1241.
Повний текст джерелаWith the growth of smart devices market to provide athletes and trainers a systematic, objective and reliable follow-up, more and more parameters are monitored for a same individual. An alternative to laboratory evaluation methods is the use of inertial sensors which allow following the performance without hindering it, without space limits and without tedious initialization procedures. Data collected by those sensors can be classified as multivariate functional data: some quantitative entities evolving along time and collected simultaneously for a same individual. The aim of this thesis is to find parameters for analysing the athlete horse locomotion thanks to a sensor put in the saddle. This connected device (inertial sensor, IMU) for equestrian sports allows the collection of acceleration and angular velocity along time in the three space directions and with a sampling frequency of 100 Hz. The database used for model development is made of 3221 canter strides from 58 ridden jumping horses of different age and level of competition. Two different protocols are used to collect data: one for straight path and one for curved path. We restricted our work to the prediction of three parameters: the speed per stride, the stride length and the jump quality. To meet the first to objectives, we developed a multivariate functional clustering method that allow the division of the database into smaller more homogeneous sub-groups from the collected signals point of view. This method allows the characterization of each group by it average profile, which ease the data understanding and interpretation. But surprisingly, this clustering model did not improve the results of speed prediction, Support Vector Machine (SVM) is the model with the lowest percentage of error above 0.6 m/s. The same applied for the stride length where an accuracy of 20 cm is reached thanks to SVM model. Those results can be explained by the fact that our database is build from 58 horses only, which is a quite low number of individuals for a clustering method. Then we extend this method to the co-clustering of multivariate functional data in order to ease the datamining of horses’ follow-up databases. This method might allow the detection and prevention of locomotor disturbances, main source of interruption of jumping horses. Lastly, we looked for correlation between jumping quality and signals collected by the IMU. First results show that signals collected by the saddle alone are not sufficient to differentiate finely the jumping quality. Additional information will be needed, for example using complementary sensors or by expanding the database to have a more diverse range of horses and jump profiles
Laclau, Charlotte. "Hard and fuzzy block clustering algorithms for high dimensional data." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB014.
Повний текст джерелаWith the increasing number of data available, unsupervised learning has become an important tool used to discover underlying patterns without the need to label instances manually. Among different approaches proposed to tackle this problem, clustering is arguably the most popular one. Clustering is usually based on the assumption that each group, also called cluster, is distributed around a center defined in terms of all features while in some real-world applications dealing with high-dimensional data, this assumption may be false. To this end, co-clustering algorithms were proposed to describe clusters by subsets of features that are the most relevant to them. The obtained latent structure of data is composed of blocks usually called co-clusters. In first two chapters, we describe two co-clustering methods that proceed by differentiating the relevance of features calculated with respect to their capability of revealing the latent structure of the data in both probabilistic and distance-based framework. The probabilistic approach uses the mixture model framework where the irrelevant features are assumed to have a different probability distribution that is independent of the co-clustering structure. On the other hand, the distance-based (also called metric-based) approach relied on the adaptive metric where each variable is assigned with its weight that defines its contribution in the resulting co-clustering. From the theoretical point of view, we show the global convergence of the proposed algorithms using Zangwill convergence theorem. In the last two chapters, we consider a special case of co-clustering where contrary to the original setting, each subset of instances is described by a unique subset of features resulting in a diagonal structure of the initial data matrix. Same as for the two first contributions, we consider both probabilistic and metric-based approaches. The main idea of the proposed contributions is to impose two different kinds of constraints: (1) we fix the number of row clusters to the number of column clusters; (2) we seek a structure of the original data matrix that has the maximum values on its diagonal (for instance for binary data, we look for diagonal blocks composed of ones with zeros outside the main diagonal). The proposed approaches enjoy the convergence guarantees derived from the results of the previous chapters. Finally, we present both hard and fuzzy versions of the proposed algorithms. We evaluate our contributions on a wide variety of synthetic and real-world benchmark binary and continuous data sets related to text mining applications and analyze advantages and inconvenients of each approach. To conclude, we believe that this thesis covers explicitly a vast majority of possible scenarios arising in hard and fuzzy co-clustering and can be seen as a generalization of some popular biclustering approaches
Ailem, Melissa. "Sparsity-sensitive diagonal co-clustering algorithms for the effective handling of text data." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB087.
Повний текст джерелаIn the current context, there is a clear need for Text Mining techniques to analyse the huge quantity of unstructured text documents available on the Internet. These textual data are often represented by sparse high dimensional matrices where rows and columns represent documents and terms respectively. Thus, it would be worthwhile to simultaneously group these terms and documents into meaningful clusters, making this substantial amount of data easier to handle and interpret. Co-clustering techniques just serve this purpose. Although many existing co-clustering approaches have been successful in revealing homogeneous blocks in several domains, these techniques are still challenged by the high dimensionality and sparsity characteristics exhibited by document-term matrices. Due to this sparsity, several co-clusters are primarily composed of zeros. While homogeneous, these co-clusters are irrelevant and must be filtered out in a post-processing step to keep only the most significant ones. The objective of this thesis is to propose new co-clustering algorithms tailored to take into account these sparsity-related issues. The proposed algorithms seek a block diagonal structure and allow to straightaway identify the most useful co-clusters, which makes them specially effective for the text co-clustering task. Our contributions can be summarized as follows: First, we introduce and demonstrate the effectiveness of a novel co-clustering algorithm based on a direct maximization of graph modularity. While existing graph-based co-clustering algorithms rely on spectral relaxation, the proposed algorithm uses an iterative alternating optimization procedure to reveal the most meaningful co-clusters in a document-term matrix. Moreover, the proposed optimization has the advantage of avoiding the computation of eigenvectors, a task which is prohibitive when considering high dimensional data. This is an improvement over spectral approaches, where the eigenvectors computation is necessary to perform the co-clustering. Second, we use an even more powerful approach to discover block diagonal structures in document-term matrices. We rely on mixture models, which offer strong theoretical foundations and considerable flexibility that makes it possible to uncover various specific cluster structure. More precisely, we propose a rigorous probabilistic model based on the Poisson distribution and the well known Latent Block Model. Interestingly, this model includes the sparsity in its formulation, which makes it particularly effective for text data. Setting the estimate of this model’s parameters under the Maximum Likelihood (ML) and the Classification Maximum Likelihood (CML) approaches, four co-clustering algorithms have been proposed, including a hard, a soft, a stochastic and a fourth algorithm which leverages the benefits of both the soft and stochastic variants, simultaneously. As a last contribution of this thesis, we propose a new biomedical text mining framework that includes some of the above mentioned co-clustering algorithms. This work shows the contribution of co-clustering in a real biomedical text mining problematic. The proposed framework is able to propose new clues about the results of genome wide association studies (GWAS) by mining PUBMED abstracts. This framework has been tested on asthma disease and allowed to assess the strength of associations between asthma genes reported in previous GWAS as well as discover new candidate genes likely associated to asthma. In a nutshell, while several text co-clustering algorithms already exist, their performance can be substantially increased if more appropriate models and algorithms are available. According to the extensive experiments done on several challenging real-world text data sets, we believe that this thesis has served well this objective
Bozdag, Doruk. "Graph Coloring and Clustering Algorithms for Science and Engineering Applications." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1229459765.
Повний текст джерелаAnand, K. "Methods for Blind Separation of Co-Channel BPSK Signals Arriving at an Antenna Array and Their Performance Analysis." Thesis, Indian Institute of Science, 1995. http://hdl.handle.net/2005/123.
Повний текст джерелаMédoc, Nicolas. "A visual analytics approach for multi-resolution and multi-model analysis of text corpora : application to investigative journalism." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCB042/document.
Повний текст джерелаAs the production of digital texts grows exponentially, a greater need to analyze text corpora arises in various domains of application, insofar as they constitute inexhaustible sources of shared information and knowledge. We therefore propose in this thesis a novel visual analytics approach for the analysis of text corpora, implemented for the real and concrete needs of investigative journalism. Motivated by the problems and tasks identified with a professional investigative journalist, visualizations and interactions are designed through a user-centered methodology involving the user during the whole development process. Specifically, investigative journalists formulate hypotheses and explore exhaustively the field under investigation in order to multiply sources showing pieces of evidence related to their working hypothesis. Carrying out such tasks in a large corpus is however a daunting endeavor and requires visual analytics software addressing several challenging research issues covered in this thesis. First, the difficulty to make sense of a large text corpus lies in its unstructured nature. We resort to the Vector Space Model (VSM) and its strong relationship with the distributional hypothesis, leveraged by multiple text mining algorithms, to discover the latent semantic structure of the corpus. Topic models and biclustering methods are recognized to be well suited to the extraction of coarse-grained topics, i.e. groups of documents concerning similar topics, each one represented by a set of terms extracted from textual contents. We provide a new Weighted Topic Map visualization that conveys a broad overview of coarse-grained topics by allowing quick interpretation of contents through multiple tag clouds while depicting the topical structure such as the relative importance of topics and their semantic similarity. Although the exploration of the coarse-grained topics helps locate topic of interest and its neighborhood, the identification of specific facts, viewpoints or angles related to events or stories requires finer level of structuration to represent topic variants. This nested structure, revealed by Bimax, a pattern-based overlapping biclustering algorithm, captures in biclusters the co-occurrences of terms shared by multiple documents and can disclose facts, viewpoints or angles related to events or stories. This thesis tackles issues related to the visualization of a large amount of overlapping biclusters by organizing term-document biclusters in a hierarchy that limits term redundancy and conveys their commonality and specificities. We evaluated the utility of our software through a usage scenario and a qualitative evaluation with an investigative journalist. In addition, the co-occurrence patterns of topic variants revealed by Bima. are determined by the enclosing topical structure supplied by the coarse-grained topic extraction method which is run beforehand. Nonetheless, little guidance is found regarding the choice of the latter method and its impact on the exploration and comprehension of topics and topic variants. Therefore we conducted both a numerical experiment and a controlled user experiment to compare two topic extraction methods, namely Coclus, a disjoint biclustering method, and hierarchical Latent Dirichlet Allocation (hLDA), an overlapping probabilistic topic model. The theoretical foundation of both methods is systematically analyzed by relating them to the distributional hypothesis. The numerical experiment provides statistical evidence of the difference between the resulting topical structure of both methods. The controlled experiment shows their impact on the comprehension of topic and topic variants, from analyst perspective. (...)
Kyrgyzov, Ivan. "Recherche dans les bases de donnees satellitaires des paysages et application au milieu urbain: clustering, consensus et categorisation." Phd thesis, Télécom ParisTech, 2008. http://pastel.archives-ouvertes.fr/pastel-00004084.
Повний текст джерелаGuo, Pei Fang. "PalmPrints : a cooperative co-evolutionary clustering algorithm for hand-based biometric identification." Thesis, 2003. http://spectrum.library.concordia.ca/2283/1/MQ83865.pdf.
Повний текст джерелаCho, Hyuk. "Co-clustering algorithms : extensions and applications." 2008. http://hdl.handle.net/2152/17809.
Повний текст джерелаtext
Книги з теми "Co-clustering algorithm"
Govaert, Gerard, and Mohamed Nadif. Co-Clustering. Wiley & Sons, Incorporated, John, 2013.
Знайти повний текст джерелаNadif, Mohamed, and Gérard Govaert. Co-Clustering: Models, Algorithms and Applications. Wiley & Sons, Incorporated, John, 2014.
Знайти повний текст джерелаNadif, Mohamed, and Gérard Govaert. Co-Clustering: Models, Algorithms and Applications. Wiley-Interscience, 2013.
Знайти повний текст джерелаNadif, Mohamed, and Gérard Govaert. Co-Clustering: Models, Algorithms and Applications. Wiley & Sons, Incorporated, John, 2013.
Знайти повний текст джерелаNadif, Mohamed, and G�rard Govaert. Co-Clustering: Models, Algorithms and Applications. Wiley & Sons, Incorporated, John, 2013.
Знайти повний текст джерелаЧастини книг з теми "Co-clustering algorithm"
Laclau, Charlotte, and Mohamed Nadif. "Diagonal Co-clustering Algorithm for Document-Word Partitioning." In Advances in Intelligent Data Analysis XIV, 170–80. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24465-5_15.
Повний текст джерелаHemalatha, Chunduru, and T. V. Sarath. "Analysis of Clustering Algorithm in VANET Through Co-Simulation." In Sustainable Communication Networks and Application, 441–50. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6605-6_32.
Повний текст джерелаKharma, Nawwaf, Ching Y. Suen, and Pei F. Guo. "PalmPrints: A Novel Co-evolutionary Algorithm for Clustering Finger Images." In Genetic and Evolutionary Computation — GECCO 2003, 322–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45105-6_38.
Повний текст джерелаWu, Meng-Lun, and Chia-Hui Chang. "Parallel Co-clustering with Augmented Matrices Algorithm with Map-Reduce." In Data Warehousing and Knowledge Discovery, 183–94. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10160-6_17.
Повний текст джерелаZhou, Xiaowei, Fumin Ma, and Mengtao Zhang. "Clustering Ensemble Algorithm Based on an Improved Co-association Matrix." In Intelligent Equipment, Robots, and Vehicles, 805–15. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7213-2_78.
Повний текст джерелаHonda, Katsuhiro, Arina Kawano, and Akira Notsu. "A Greedy Fuzzy k-Member Co-clustering Algorithm and Collaborative Filtering Applicability." In Knowledge-Based Information Systems in Practice, 39–50. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13545-8_3.
Повний текст джерелаTjhi, William-Chandra, and Lihui Chen. "A New Fuzzy Co-clustering Algorithm for Categorization of Datasets with Overlapping Clusters." In Advanced Data Mining and Applications, 328–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_36.
Повний текст джерелаKwon, Bongjune, and Hyuk Cho. "Scalable Co-clustering Algorithms." In Algorithms and Architectures for Parallel Processing, 32–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13119-6_3.
Повний текст джерелаBulteau, Laurent, Vincent Froese, Sepp Hartung, and Rolf Niedermeier. "Co-Clustering Under the Maximum Norm." In Algorithms and Computation, 298–309. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13075-0_24.
Повний текст джерелаHonda, Katsuhiro. "Fuzzy Clustering/Co-clustering and Probabilistic Mixture Models-Induced Algorithms." In Fuzzy Sets, Rough Sets, Multisets and Clustering, 29–43. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47557-8_3.
Повний текст джерелаТези доповідей конференцій з теми "Co-clustering algorithm"
Tjhi, William-Chandra, and Lihui Chen. "Robust fuzzy Co-clustering algorithm." In 2007 6th International Conference on Information, Communications & Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icics.2007.4449868.
Повний текст джерелаVan Nha Pham and Long Thanh Ngo. "Interval type-2 fuzzy co-clustering algorithm." In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2015. http://dx.doi.org/10.1109/fuzz-ieee.2015.7337960.
Повний текст джерелаNicoleta, Rogovschi, Lazhar Labiod, and Mohamed Nadif. "A spectral algorithm for topographical Co-clustering." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252398.
Повний текст джерелаNarang, Ankur, Abhinav Srivastava, and Naga Praveen Kumar Katta. "Distributed hierarchical co-clustering and collaborative filtering algorithm." In 2012 19th International Conference on High Performance Computing (HiPC). IEEE, 2012. http://dx.doi.org/10.1109/hipc.2012.6507497.
Повний текст джерелаHoseini, Elham, Sattar Hashemi, and Ali Hamzeh. "A levelwise spectral co-clustering algorithm for collaborative filtering." In the 6th International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2184751.2184759.
Повний текст джерелаBinh, Le Thi Cam, Pham Van Nha, and Pham The Long. "Fuzzy Co-clustering Algorithm for Multi-source Data Mining." In 19th World Congress of the International Fuzzy Systems Association (IFSA), 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and 11th International Summer School on Aggregation Operators (AGOP). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/asum.k.210827.016.
Повний текст джерелаYao, Shixin, Guoxian Yu, Jun Wang, Carlotta Domeniconi, and Xiangliang Zhang. "Multi-View Multiple Clustering." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/572.
Повний текст джерелаPham, Van Nha, Long Thanh Ngo, and Thao Duc Nguyen. "Feature-reduction fuzzy co-clustering algorithm for hyperspectral image segmentation." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015643.
Повний текст джерелаRamanathan, Venkatram, Wenjing Ma, Vignesh T. Ravi, Tantan Liu, and Gagan Agrawal. "Parallelizing an Information Theoretic Co-clustering Algorithm Using a Cloud Middleware." In 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.100.
Повний текст джерелаLu, Wei, and Ling Xue. "A Heuristic-Based Co-clustering Algorithm for the Internet Traffic Classification." In 2014 28th International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE, 2014. http://dx.doi.org/10.1109/waina.2014.16.
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