Littérature scientifique sur le sujet « HYBRID CLUSTERING »
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Articles de revues sur le sujet "HYBRID CLUSTERING"
Osei-Bryson, Kweku-Muata, et Tasha R. Inniss. « A hybrid clustering algorithm ». Computers & ; Operations Research 34, no 11 (novembre 2007) : 3255–69. http://dx.doi.org/10.1016/j.cor.2005.12.004.
Texte intégralIkotun, Abiodun M., et Absalom E. Ezugwu. « Boosting k-means clustering with symbiotic organisms search for automatic clustering problems ». PLOS ONE 17, no 8 (11 août 2022) : e0272861. http://dx.doi.org/10.1371/journal.pone.0272861.
Texte intégralAugusteijn, M. F., et U. J. Steck. « Supervised adaptive clustering : A hybrid neural network clustering algorithm ». Neural Computing & ; Applications 7, no 1 (mars 1998) : 78–89. http://dx.doi.org/10.1007/bf01413712.
Texte intégralYu, Zhiwen, Le Li, Yunjun Gao, Jane You, Jiming Liu, Hau-San Wong et Guoqiang Han. « Hybrid clustering solution selection strategy ». Pattern Recognition 47, no 10 (octobre 2014) : 3362–75. http://dx.doi.org/10.1016/j.patcog.2014.04.005.
Texte intégralAmiri, Saeid, Bertrand S. Clarke, Jennifer L. Clarke et Hoyt Koepke. « A General Hybrid Clustering Technique ». Journal of Computational and Graphical Statistics 28, no 3 (18 mars 2019) : 540–51. http://dx.doi.org/10.1080/10618600.2018.1546593.
Texte intégralJaved, Ali, et Byung Suk Lee. « Hybrid semantic clustering of hashtags ». Online Social Networks and Media 5 (mars 2018) : 23–36. http://dx.doi.org/10.1016/j.osnem.2017.10.004.
Texte intégralChen, Yan, et Qin Zhou Niu. « Hybrid Clustering Algorithm Based on KNN and MCL ». Applied Mechanics and Materials 610 (août 2014) : 302–6. http://dx.doi.org/10.4028/www.scientific.net/amm.610.302.
Texte intégralP. Saveetha, P. Saveetha, Y. Harold Robinson P. Saveetha, Vimal Shanmuganathan Y. Harold Robinson, Seifedine Kadry Vimal Shanmuganathan et Yunyoung Nam Seifedine Kadry. « Hybrid Energy-based Secured Clustering technique for Wireless Sensor Networks ». 網際網路技術學刊 23, no 1 (janvier 2022) : 021–31. http://dx.doi.org/10.53106/160792642022012301003.
Texte intégralLIU, YONGGUO, XIAORONG PU, YIDONG SHEN, ZHANG YI et XIAOFENG LIAO. « CLUSTERING USING AN IMPROVED HYBRID GENETIC ALGORITHM ». International Journal on Artificial Intelligence Tools 16, no 06 (décembre 2007) : 919–34. http://dx.doi.org/10.1142/s021821300700362x.
Texte intégralYang, Wenlu, Yinghui Zhang, Hongjun Wang, Ping Deng et Tianrui Li. « Hybrid genetic model for clustering ensemble ». Knowledge-Based Systems 231 (novembre 2021) : 107457. http://dx.doi.org/10.1016/j.knosys.2021.107457.
Texte intégralThèses sur le sujet "HYBRID CLUSTERING"
Keller, Jens. « Clustering biological data using a hybrid approach : Composition of clusterings from different features ». Thesis, University of Skövde, School of Humanities and Informatics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-1078.
Texte intégralClustering of data is a well-researched topic in computer sciences. Many approaches have been designed for different tasks. In biology many of these approaches are hierarchical and the result is usually represented in dendrograms, e.g. phylogenetic trees. However, many non-hierarchical clustering algorithms are also well-established in biology. The approach in this thesis is based on such common algorithms. The algorithm which was implemented as part of this thesis uses a non-hierarchical graph clustering algorithm to compute a hierarchical clustering in a top-down fashion. It performs the graph clustering iteratively, with a previously computed cluster as input set. The innovation is that it focuses on another feature of the data in each step and clusters the data according to this feature. Common hierarchical approaches cluster e.g. in biology, a set of genes according to the similarity of their sequences. The clustering then reflects a partitioning of the genes according to their sequence similarity. The approach introduced in this thesis uses many features of the same objects. These features can be various, in biology for instance similarities of the sequences, of gene expression or of motif occurences in the promoter region. As part of this thesis not only the algorithm itself was implemented and evaluated, but a whole software also providing a graphical user interface. The software was implemented as a framework providing the basic functionality with the algorithm as a plug-in extending the framework. The software is meant to be extended in the future, integrating a set of algorithms and analysis tools related to the process of clustering and analysing data not necessarily related to biology.
The thesis deals with topics in biology, data mining and software engineering and is divided into six chapters. The first chapter gives an introduction to the task and the biological background. It gives an overview of common clustering approaches and explains the differences between them. Chapter two shows the idea behind the new clustering approach and points out differences and similarities between it and common clustering approaches. The third chapter discusses the aspects concerning the software, including the algorithm. It illustrates the architecture and analyses the clustering algorithm. After the implementation the software was evaluated, which is described in the fourth chapter, pointing out observations made due to the use of the new algorithm. Furthermore this chapter discusses differences and similarities to related clustering algorithms and software. The thesis ends with the last two chapters, namely conclusions and suggestions for future work. Readers who are interested in repeating the experiments which were made as part of this thesis can contact the author via e-mail, to get the relevant data for the evaluation, scripts or source code.
Tyree, Eric William. « A hybrid methodology for data clustering ». Thesis, City University London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301057.
Texte intégralMoore, Garrett Lee. « A Hybrid (Active-Passive) VANET Clustering Technique ». Diss., NSUWorks, 2019. https://nsuworks.nova.edu/gscis_etd/1077.
Texte intégralGurcan, Fatih. « A Hybrid Movie Recommender Using Dynamic Fuzzy Clustering ». Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/2/12611667/index.pdf.
Texte intégralTantrum, Jeremy. « Model based and hybrid clustering of large datasets / ». Thesis, Connect to this title online ; UW restricted, 2003. http://hdl.handle.net/1773/8933.
Texte intégralGarbiso, Julian Pedro. « Fair auto-adaptive clustering for hybrid vehicular networks ». Thesis, Paris, ENST, 2017. http://www.theses.fr/2017ENST0061/document.
Texte intégralFor the development of innovative Intelligent Transportation Systems applications, connected vehicles will frequently need to upload and download position-based information to and from servers. These vehicles will be equipped with different Radio Access Technologies (RAT), like cellular and vehicle-to-vehicle (V2V) technologies such as LTE and IEEE 802.11p respectively. Cellular networkscan provide internet access almost anywhere, with QoS guarantees. However, accessing these networks has an economic cost. In this thesis, a multi-hop clustering algorithm is proposed in the aim of reducing the cellular access costs by aggregating information and off-loading data in the V2V network, using the Cluster Head as a single gateway to the cellular network. For the example application of uploading aggregated Floating Car Data, simulation results show that this approach reduce cellular data consumption by more than 80% by reducing the typical redundancy of position-based data in a vehicular network. There is a threefold contribution: First, an approach that delegates the Cluster Head selection to the cellular base station in order to maximize the cluster size, thus maximizing aggregation. Secondly, a self-adaptation algorithm that dynamically changes the maximum number of hops, addressing the trade-off between cellular access reduction and V2V packet loss. Finally, the incorporation of a theory of distributive justice, for improving fairness over time regarding the distribution of the cost in which Cluster Heads have to incur, thus improving the proposal’s social acceptability. The proposed algorithms were tested via simulation, and the results show a significant reduction in cellular network usage, a successful adaptation of the number of hops to changes in the vehicular traffic density, and an improvement in fairness metrics, without affecting network performance
Javed, Ali. « A Hybrid Approach to Semantic Hashtag Clustering in Social Media ». ScholarWorks @ UVM, 2016. http://scholarworks.uvm.edu/graddis/623.
Texte intégralGARRAFFA, MICHELE. « Exact and Heuristic Hybrid Approaches for Scheduling and Clustering Problems ». Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2639115.
Texte intégralMasoudi, Pedram. « Application of hybrid uncertainty-clustering approach in pre-processing well-logs ». Thesis, Rennes 1, 2017. http://www.theses.fr/2017REN1S023/document.
Texte intégralIn the subsurface geology, characterization of geological beds by well-logs is an uncertain task. The thesis mainly concerns studying vertical resolution of well-logs (question 1). In the second stage, fuzzy arithmetic is applied to experimental petrophysical relations to project the uncertainty range of the inputs to the outputs, here irreducible water saturation and permeability (question 2). Regarding the first question, the logging mechanism is modelled by fuzzy membership functions. Vertical resolution of membership function (VRmf) is larger than spacing and sampling rate. Due to volumetric mechanism of logging, volumetric Nyquist frequency is proposed. Developing a geometric simulator for generating synthetic-logs of a single thin-bed enabled us analysing sensitivity of the well-logs to the presence of a thin-bed. Regression-based relations between ideal-logs (simulator inputs) and synthetic-logs (simulator outputs) are used as deconvolution relations for removing shoulder-bed effect of thin-beds from GR, RHOB and NPHI well-logs. NPHI deconvolution relation is applied to a real case where the core porosity of a thin-bed is 8.4%. The NPHI well-log is 3.8%, and the deconvolved NPHI is 11.7%. Since it is not reasonable that the core porosity (effective porosity) be higher than the NPHI (total porosity), the deconvolved NPHI is more accurate than the NPHI well-log. It reveals that the shoulder-bed effect is reduced in this case. The thickness of the same thin-bed was also estimated to be 13±7.5 cm, which is compatible with the thickness of the thin-bed in the core box (<25 cm). Usually, in situ thickness is less than the thickness of the core boxes, since at the earth surface, there is no overburden pressure, also the cores are weathered. Dempster-Shafer Theory (DST) was used to create well-log uncertainty range. While the VRmf of the well-logs is more than 60 cm, the VRmf of the belief and plausibility functions (boundaries of the uncertainty range) would be about 15 cm. So, the VRmf is improved, while the certainty of the well-log value is lost. In comparison with geometric method, DST-based algorithm resulted in a smaller uncertainty range of GR, RHOB and NPHI logs by 100%, 71% and 66%, respectively. In the next step, cluster analysis is applied to NPHI, RHOB and DT for the purpose of providing cluster-based uncertainty range. Then, NPHI is calibrated by core porosity value in each cluster, showing low √MSE compared to the five conventional porosity estimation models (at least 33% of improvement in √MSE). Then, fuzzy arithmetic is applied to calculate fuzzy numbers of irreducible water saturation and permeability. Fuzzy number of irreducible water saturation provides better (less overestimation) results than the crisp estimation. It is found that when the cluster interval of porosity is not compatible with the core porosity, the permeability fuzzy numbers are not valid, e.g. in well#4. Finally, in the possibilistic approach (the fuzzy theory), by calibrating α-cut, the right uncertainty interval could be achieved, concerning the scale of the study
Hung, Chih-Li. « An adaptive SOM model for document clustering using hybrid neural techniques ». Thesis, University of Sunderland, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400460.
Texte intégralLivres sur le sujet "HYBRID CLUSTERING"
De, Sourav, Sandip Dey et Siddhartha Bhattacharyya, dir. Recent Advances in Hybrid Metaheuristics for Data Clustering. Wiley, 2020. http://dx.doi.org/10.1002/9781119551621.
Texte intégralBhattacharyya, Siddhartha, Sourav De et Sandip Dey. Recent Advances in Hybrid Metaheuristics for Data Clustering. Wiley & Sons, Limited, John, 2020.
Trouver le texte intégralBhattacharyya, Siddhartha, Sourav De et Sandip Dey. Recent Advances in Hybrid Metaheuristics for Data Clustering. Wiley & Sons, Incorporated, John, 2020.
Trouver le texte intégralRecent Advances in Hybrid Metaheuristics for Data Clustering. Wiley & Sons, Limited, John, 2020.
Trouver le texte intégralBhattacharyya, Siddhartha, Sourav De et Sandip Dey. Recent Advances in Hybrid Metaheuristics for Data Clustering. Wiley & Sons, Incorporated, John, 2020.
Trouver le texte intégralKaur, Arvind, et Nancy Nancy. Comparative Analysis of Hybrid Clustering Algorithm with K- Means. Independently Published, 2018.
Trouver le texte intégralChapitres de livres sur le sujet "HYBRID CLUSTERING"
Awange, Joseph, Béla Paláncz et Lajos Völgyesi. « Clustering ». Dans Hybrid Imaging and Visualization, 149–95. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26153-5_3.
Texte intégralPrzybyła, Tomasz. « Hybrid Fuzzy Clustering Method ». Dans Advances in Soft Computing, 60–67. Berlin, Heidelberg : Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75175-5_8.
Texte intégralKogan, Jacob, Charles Nicholas et Mike Wiacek. « Hybrid Clustering with Divergences ». Dans Survey of Text Mining II, 65–85. London : Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-046-9_4.
Texte intégralSingh, Tribhuvan, Krishn Kumar Mishra et Ranvijay. « Data Clustering Using Environmental Adaptation Method ». Dans Hybrid Intelligent Systems, 156–64. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49336-3_16.
Texte intégralOliveira, A. C. M., et L. A. N. Lorena. « Hybrid Evolutionary Algorithms and Clustering Search ». Dans Hybrid Evolutionary Algorithms, 77–99. Berlin, Heidelberg : Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_4.
Texte intégralPanwar, Surya Nandan, Saliya Goyal et Prafulla Bafna. « Analytical Study of Starbucks Using Clustering ». Dans Hybrid Intelligent Systems, 1013–21. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27409-1_93.
Texte intégralChong, A., T. D. Gedeon et K. W. Wong. « Histogram-Based Fuzzy Clustering and Its Comparison to Fuzzy C-Means Clustering in One-Dimensional Data ». Dans Hybrid Information Systems, 253–67. Heidelberg : Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1782-9_19.
Texte intégralSarkar, Jnanendra Prasad, Indrajit Saha, Anasua Sarkar et Ujjwal Maulik. « Improving Modified Differential Evolution for Fuzzy Clustering ». Dans Hybrid Intelligent Systems, 136–46. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76351-4_14.
Texte intégralSmith, Kate A., Sheldon Chuan et Peter Putten. « Determining the Validity of Clustering for Data Fusion ». Dans Hybrid Information Systems, 627–36. Heidelberg : Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1782-9_45.
Texte intégralBhoi, Swati G., et Ujwala M. Patil. « Hybrid Clustering Based Smart Crawler ». Dans Communications in Computer and Information Science, 137–44. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1423-0_16.
Texte intégralActes de conférences sur le sujet "HYBRID CLUSTERING"
Chandra, B. « Hybrid clustering algorithm ». Dans 2009 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2009. http://dx.doi.org/10.1109/icsmc.2009.5346251.
Texte intégralSrinoy, Surat, et Werasak Kurutach. « Combination Artificial Ant Clustering and K-PSO Clustering Approach to Network Security Model ». Dans 2006 International Conference on Hybrid Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/ichit.2006.253601.
Texte intégralJiang, Sheng-Yi, et Xia Li. « A Hybrid Clustering Algorithm ». Dans 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2009. http://dx.doi.org/10.1109/fskd.2009.93.
Texte intégralNiennattrakul, V., et C. A. Ratanamahatana. « Clustering Multimedia Data Using Time Series ». Dans 2006 International Conference on Hybrid Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/ichit.2006.253514.
Texte intégralHwang, Junwon, Dooheon Song et Changhoon Lee. « Performance Analysis of 2-tier Clustering ». Dans 2006 International Conference on Hybrid Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/ichit.2006.253659.
Texte intégralFaceli, Katti, Andre De Carvalho et Marcilio De Souto. « Multi-Objective Clustering Ensemble ». Dans 2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06). IEEE, 2006. http://dx.doi.org/10.1109/his.2006.264934.
Texte intégralYoo, Jungsoon Park, Chrisila C. Pettey et Sung Yoo. « A hybrid conceptual clustering system ». Dans the 1996 ACM 24th annual conference. New York, New York, USA : ACM Press, 1996. http://dx.doi.org/10.1145/228329.228341.
Texte intégralOduntan, Olayinka Idowu, et Parimala Thulasiraman. « Hybrid Metaheuristic Algorithm for Clustering ». Dans 2018 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2018. http://dx.doi.org/10.1109/ssci.2018.8628863.
Texte intégralDehshibi, Mohammad Mahdi, Meysam Alavi et Jamshid Shanbehzadeh. « Kernel-based Persian viseme clustering ». Dans 2013 13th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2013. http://dx.doi.org/10.1109/his.2013.6920468.
Texte intégralChimphlee, W., A. H. Abdullah, M. Noor Md Sap, S. Srinoy et S. Chimphlee. « Anomaly-Based Intrusion Detection using Fuzzy Rough Clustering ». Dans 2006 International Conference on Hybrid Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/ichit.2006.253508.
Texte intégralRapports d'organisations sur le sujet "HYBRID CLUSTERING"
Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak et Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, juillet 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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