Relevant bibliographies by topics / Clustering-based approach

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  2. Dissertations / Theses
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Academic literature on the topic 'Clustering-based approach'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Clustering-based approach"

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Feng, Yuxiang, Simon Pickering, Edward Chappell, Pejman Iravani, and Chris Brace. "A Support Vector Clustering Based Approach for Driving Style Classification." International Journal of Machine Learning and Computing 9, no. 3 (2019): 344–50. http://dx.doi.org/10.18178/ijmlc.2019.9.3.808.

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Lubbad, Mohammed A. H., and Wesam M. Ashour. "Cosine-Based Clustering Algorithm Approach." International Journal of Intelligent Systems and Applications 4, no. 1 (2012): 53–63. http://dx.doi.org/10.5815/ijisa.2012.01.07.

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Ahmed, Soraya Setti, Mimoun Malki, and Sidi Mohamed Benslimane. "Ontology Partitioning: Clustering Based Approach." International Journal of Information Technology and Computer Science 7, no. 6 (2015): 1–11. http://dx.doi.org/10.5815/ijitcs.2015.06.01.

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Kaur, Supreet. "Quality Prediction of Object Oriented Software Using Density Based Clustering Approach." International Journal of Engineering and Technology 3, no. 4 (2011): 440–45. http://dx.doi.org/10.7763/ijet.2011.v3.267.

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Jayaneththi, J. K. D. B. G., and Banage T. G. S. Kumara. "Clustering-Based Approach for Clustering Journals in Computer Science." International Journal of Systems and Service-Oriented Engineering 9, no. 2 (2019): 35–51. http://dx.doi.org/10.4018/ijssoe.2019040103.

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In the present scientific world, most of the authors of scientific literature are seeking effective ways to share their research findings with large peer groups. But finding a high-quality journal to publish paper is a huge challenge. Most of the journals present today are predatory and less-quality. The main aim of this study is to help the researchers in identifying the quality level of computer science journals by introducing a data mining model based on six journal quality metrics (Journal Impact Factor, SCImago Journal Rank, Eigenfactor, H-index, Source Normalized Impact per Paper, and Ar
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Kampffmeyer, Michael, Sigurd Løkse, Filippo M. Bianchi, Lorenzo Livi, Arnt-Børre Salberg, and Robert Jenssen. "Deep divergence-based approach to clustering." Neural Networks 113 (May 2019): 91–101. http://dx.doi.org/10.1016/j.neunet.2019.01.015.

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Yuan Huang, and Bing Li. "A Similarity-Based Service Clustering Approach." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 21 (2012): 613–21. http://dx.doi.org/10.4156/aiss.vol4.issue21.78.

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Laloë, Thomas. "Quantization based clustering: An iterative approach." Pattern Recognition Letters 142 (February 2021): 51–57. http://dx.doi.org/10.1016/j.patrec.2020.12.007.

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Ahmadi, Abbas, Fakhri Karray, and Mohamed S. Kamel. "Flocking based approach for data clustering." Natural Computing 9, no. 3 (2009): 767–91. http://dx.doi.org/10.1007/s11047-009-9173-5.

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Zhang, De-gan, Hong-li Niu, and Si Liu. "Novel PEECR-based clustering routing approach." Soft Computing 21, no. 24 (2016): 7313–23. http://dx.doi.org/10.1007/s00500-016-2270-3.

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Dissertations / Theses on the topic "Clustering-based approach"

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Shekar, B. "A Knowledge-Based Approach To Pattern Clustering." Thesis, Indian Institute of Science, 1988. http://hdl.handle.net/2005/86.

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The primary objective of this thesis is to develop a methodology for clustering of objects based on their functionality typified by the notion of concept. We begin by giving a formal definition of concept. By assigning a functional interpretation to the underlying concept, we demonstrate the applicability of the functionally interpreted concept for clustering objects. This functional interpretation leads us to identifying two classes of concepts, namely, the Necessary class and the Quality-Improvement class. Next, we categorize the functional cohesiveness among objects into three different cla
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Hossain, Mahmud Shahriar. "Apriori approach to graph-based clustering of text documents." Thesis, Montana State University, 2008. http://etd.lib.montana.edu/etd/2008/hossain/HossainM0508.pdf.

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This thesis report introduces a new technique of document clustering based on frequent senses. The developed system, named GDClust (Graph-Based Document Clustering) [1], works with frequent senses rather than dealing with frequent keywords used in traditional text mining techniques. GDClust presents text documents as hierarchical document-graphs and uses an Apriori paradigm to find the frequent subgraphs, which reflect frequent senses. Discovered frequent subgraphs are then utilized to generate accurate sense-based document clusters. We propose a novel multilevel Gaussian minimum support strat
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Kotala, Pratap. "Usability Construct for Mobile Applications: A Clustering based Approach." Diss., North Dakota State University, 2015. https://hdl.handle.net/10365/27027.

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The growth of mobile applications that run on cell phones and other handheld devices has introduced a broad range of usability challenges that were not faced by the web and standalone PC environments. The current usability models for mobile applications are mostly based on the experience of the usability experts and users that were collected through surveys and field studies. Many usability researchers and practitioners have developed conceptual usability frameworks that utilize either different or overlapping usability attributes. Moreover, the usability frameworks in existence they are limit
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Jurek, Anna. "A new approach to classifier ensemble learning based on clustering." Thesis, Ulster University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.591073.

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The problem of combining multiple classifiers, referred to as a classifier ensemble, is one sub-domain of machine learning which has received significant attention in recent years. A classifier ensemble is an integration of classification models, referred to as base classifiers, whose individual decisions are combined in order to obtain a final prediction. The aim of using a classifier ensemble is to provide an overall level of performance which is superior to the performance of any of the single base classifiers. This Thesis studies the problem of constructing a classifier ensemble from diffe
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Palma, Andrey Luis Tietbohl. "A clustering-based approach for discovering interesting places in trajectories." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2008. http://hdl.handle.net/10183/17024.

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Por causa da grande quantidade de dados de trajetórias producidos por dispositivos móveis, existe um aumento crescente das necessidades de mecanismos para extrair conhecimento a partir desses dados. A maioria dos trabalhos existentes focam nas propriedades geometricas das trajetorias, mas recentemente surgiu o conceito de trajetórias semânticas, nas quais a informação da geografia por baixo da trajetória é integrada aos pontos da trajetória. Nesse novo conceito, trajetórias são observadas como um conjunto de stops e moves, onde stops são as partes mais importantes da trajetória. Os stops e mov
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Manickam, Sameer. "Clustering-based approach for the localization of Human Brain Nuclei." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284443.

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The study of brain nuclei in neuroimaging poses challenges owing to its small size. Many neuroimaging studies have been reported for effectively locating these nuclei and characterizing their functional connectivity with other regions of the brain. Hypothalamus, Locus Coeruleus, and Ventral Tegmental area are such nuclei found in the human brain, which are challenging to visualize owing to their size and lack of tissue contrast with surrounding regions. Resting-state functional magnetic resonance imaging (rsfMRI) analysis on these nuclei enabled researchers to characterize their connectivity w
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Al-Azab, Fadwa Gamal Mohammed. "An Improved Density-Based Clustering Algorithm Using Gravity and Aging Approaches." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/31994.

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Density-based clustering is one of the well-known algorithms focusing on grouping samples according to their densities. In the existing density-based clustering algorithms, samples are clustered according to the total number of points within the radius of the defined dense region. This method of determining density, however, provides little knowledge about the similarities among points. Additionally, they are not flexible enough to deal with dynamic data that changes over time. The current study addresses these challenges by proposing a new approach that incorporates new measures to evaluate t
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Leung, Wing-sze, and 梁穎思. "Filtering of false positive microRNA candidates by a clustering-based approach." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B41633908.

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Leung, Wing-sze. "Filtering of false positive microRNA candidates by a clustering-based approach." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B41633908.

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Leung, Kam Shek Simon. "Image processing by region extraction using a clustering approach based on colour." Thesis, University of Stirling, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.281109.

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Books on the topic "Clustering-based approach"

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Bonnay, Denis. A Clustering-Based Approach to Collective Beliefs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190680534.003.0008.

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In this chapter, I argue in favor of a new approach to collective beliefs in unorganized groups, in terms of doxastic clustering. When a group does not have dedicated mechanisms for production of collective beliefs, and when individual beliefs of members of the group are diverse, it does not make much sense to attribute to the group some average beliefs or any other kind of collective beliefs produced by aggregating individual beliefs. Rather, beliefs are meaningfully attributed to coherent subgroups of individuals who share similar opinions. In this case, attribution of collective beliefs inv
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Gries, Stefan Th, and Martin Hilpert. Variability-based neighbor clustering: A bottom-up approach to periodization in historical linguistics. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199922765.013.0014.

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Book chapters on the topic "Clustering-based approach"

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Streekmann, Niels. "MARE Approach." In Clustering-Based Support for Software Architecture Restructuring. Vieweg+Teubner Verlag, 2012. http://dx.doi.org/10.1007/978-3-8348-8675-0_6.

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He, Yulan, Siu Cheung Hui, and Yongxiang Sim. "A Novel Ant-Based Clustering Approach for Document Clustering." In Information Retrieval Technology. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11880592_43.

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Vega-Pons, Sandro, and José Ruiz-Shulcloper. "Partition Selection Approach for Hierarchical Clustering Based on Clustering Ensemble." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16687-7_69.

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Saha, Moumita. "A Graph Based Approach to Multiview Clustering." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45062-4_17.

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Elghazel, Haytham, Khalid Benabdeslem, and Alain Dussauchoy. "Constrained Graph b-Coloring Based Clustering Approach." In Data Warehousing and Knowledge Discovery. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74553-2_24.

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Duan, Songyun, Achille Fokoue, Kavitha Srinivas, and Brian Byrne. "A Clustering-Based Approach to Ontology Alignment." In The Semantic Web – ISWC 2011. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25073-6_10.

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Şerban, Gabriela, and Alina Câmpan. "Incremental Clustering Using a Core-Based Approach." In Computer and Information Sciences - ISCIS 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11569596_87.

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Lassoued, Zeineb, and Kamel Abderrahim. "PWARX Model Identification Based on Clustering Approach." In Complex System Modelling and Control Through Intelligent Soft Computations. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12883-2_6.

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Meng, Fanrong, Xuejiao Tong, and Zhixiao Wang. "A Clustering-Ensemble Approach Based on Voting." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23881-9_55.

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Berkani, Lamia. "Social-Based Collaborative Recommendation: Bees Swarm Optimization Based Clustering Approach." In Model and Data Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32065-2_11.

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Conference papers on the topic "Clustering-based approach"

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Murena, Pierre-Alexandre, Jérémie Sublime, Basarab Matei, and Antoine Cornuéjols. "An Information Theory based Approach to Multisource Clustering." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/358.

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Clustering is a compression task which consists in grouping similar objects into clusters. In real-life applications, the system may have access to several views of the same data and each view may be processed by a specific clustering algorithm: this framework is called multi-view clustering and can benefit from algorithms capable of exchanging information between the different views. In this paper, we consider this type of unsupervised ensemble learning as a compression problem and develop a theoretical framework based on algorithmic theory of information suitable for multi-view clustering an
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Wei Zhao, Junsheng Zhang, and Yunchuan Sun. "Visualization approach based on multimedia clustering." In 2008 First IEEE International Conference on Ubi-media Computing (U-Media 2008). IEEE, 2008. http://dx.doi.org/10.1109/umedia.2008.4570888.

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Zhenya Zhang, Hongmei Cheng, and Shuguang Zhang. "Approach to SOM based correlation clustering." In 2008 Chinese Control and Decision Conference (CCDC). IEEE, 2008. http://dx.doi.org/10.1109/ccdc.2008.4597772.

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Rodriguez-Siu, Kevin Christian, Dennis Barrios-Aranibar, and Raquel E. Patino-Escarcina. "Essence-Based Clustering: A multi-strategic and highly-customizable clustering approach." In 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI). IEEE, 2016. http://dx.doi.org/10.1109/la-cci.2016.7885724.

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Hamid, Isredza Rahmi A., and Jemal H. Abawajy. "Profiling Phishing Email Based on Clustering Approach." In 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2013. http://dx.doi.org/10.1109/trustcom.2013.76.

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Nishant, Potnuru Sai, Shashi Mehrotra, Patchava Ramya Sree, and Potta Srikanth. "Hierarchical clustering based Intelligent Information Retrieval approach." In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2020. http://dx.doi.org/10.1109/icssit48917.2020.9214231.

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Gang Li and Fei Liu. "A clustering-based approach on sentiment analysis." In 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, 2010. http://dx.doi.org/10.1109/iske.2010.5680859.

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Usgurlu, Burak, Ozden Ozcan Top, Erdir Ungan, and Onur Demirors. "A Clustering Based Functional Similarity Measurement Approach." In 2010 36th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2010. http://dx.doi.org/10.1109/seaa.2010.51.

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Khan, Bilal, Shaleeza Sohail, and M. Younus Javed. "Evolution Strategy Based Automated Software Clustering Approach." In Its Applications (ASEA). IEEE, 2008. http://dx.doi.org/10.1109/asea.2008.17.

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Laloux, Jean-Francois, Nhien-An Le-Khac, and M.-Tahar Kechadi. "Efficient Distributed Approach for Density-Based Clustering." In 2011 20th IEEE International Workshop On Enabling Technologies: Infrastructure For Collaborative Enterprises (WETICE). IEEE, 2011. http://dx.doi.org/10.1109/wetice.2011.27.

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Reports on the topic "Clustering-based approach"

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Oh, Man-Suk, and Adrian Raftery. Model-based Clustering with Dissimilarities: A Bayesian Approach. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada459759.

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Wang, Chih-Hao, and Na Chen. Do Multi-Use-Path Accessibility and Clustering Effect Play a Role in Residents' Choice of Walking and Cycling? Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2011.

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The transportation studies literature recognizes the relationship between accessibility and active travel. However, there is limited research on the specific impact of walking and cycling accessibility to multi-use paths on active travel behavior. Combined with the culture of automobile dependency in the US, this knowledge gap has been making it difficult for policy-makers to encourage walking and cycling mode choices, highlighting the need to promote a walking and cycling culture in cities. In this case, a clustering effect (“you bike, I bike”) can be used as leverage to initiate such a trend
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