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Relevant bibliographies by topics / Clustering theory

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Academic literature on the topic 'Clustering theory'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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Journal articles on the topic "Clustering theory"

1

Hartigan, J. A. "Statistical theory in clustering." Journal of Classification 2, no. 1 (1985): 63–76. http://dx.doi.org/10.1007/bf01908064.

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Hameeda, M., Angelo Plastino, and M. C. Rocca. "Galaxies’ clustering generalized theory." Physics of the Dark Universe 32 (May 2021): 100816. http://dx.doi.org/10.1016/j.dark.2021.100816.

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Chan, Chung, Ali Al-Bashabsheh, Qiaoqiao Zhou, Tarik Kaced, and Tie Liu. "Info-Clustering: A Mathematical Theory for Data Clustering." IEEE Transactions on Molecular, Biological and Multi-Scale Communications 2, no. 1 (2016): 64–91. http://dx.doi.org/10.1109/tmbmc.2016.2630054.

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Plewczyński, Dariusz. "Landau theory of social clustering." Physica A: Statistical Mechanics and its Applications 261, no. 3-4 (1998): 608–17. http://dx.doi.org/10.1016/s0378-4371(98)00349-5.

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Dougherty, Edward R., and Marcel Brun. "A probabilistic theory of clustering." Pattern Recognition 37, no. 5 (2004): 917–25. http://dx.doi.org/10.1016/j.patcog.2003.10.003.

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Ghoniem, Nasr M. "Clustering theory of atomic defects." Radiation Effects and Defects in Solids 148, no. 1-4 (1999): 269–318. http://dx.doi.org/10.1080/10420159908229097.

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LIU, YONGLI, YUANXIN OUYANG, and ZHANG XIONG. "INCREMENTAL CLUSTERING USING INFORMATION BOTTLENECK THEORY." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 05 (2011): 695–712. http://dx.doi.org/10.1142/s0218001411008622.

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Document clustering is one of the most effective techniques to organize documents in an unsupervised manner. In this paper, an Incremental method for document Clustering based on Information Bottleneck theory (ICIB) is presented. The ICIB is designed to improve the accuracy and efficiency of document clustering, and resolve the issue that an arbitrary choice of document similarity measure could produce an inaccurate clustering result. In our approach, document similarity is calculated using information bottleneck theory and documents are grouped incrementally. A first document is selected rand

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Guha, S., A. Meyerson, N. Mishra, R. Motwani, and L. O'Callaghan. "Clustering data streams: theory and practice." IEEE Transactions on Knowledge and Data Engineering 15, no. 3 (2003): 515–28. http://dx.doi.org/10.1109/tkde.2003.1198387.

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Zaroubi, Saleem, and Yehuda Hoffman. "Clustering in Redshift Space: Linear Theory." Astrophysical Journal 462 (May 1996): 25. http://dx.doi.org/10.1086/177124.

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Panagiotakis, Costas, Emmanuel Ramasso, Paraskevi Fragopoulou, and Daniel Aloise. "Theory and Applications of Data Clustering." Mathematical Problems in Engineering 2016 (2016): 1–2. http://dx.doi.org/10.1155/2016/5427923.

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Dissertations / Theses on the topic "Clustering theory"

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Pahmp, Oliver. "N-sphere Clustering." Thesis, Umeå universitet, Statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172387.

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This thesis introduces n-sphere clustering, a new method of cluster analysis, akin to agglomerative hierarchical clustering. It relies on expanding n-spheres around each observation until they intersect. It then clusters observations based on these intersects, the distance between the spheres, and density of observations. Currently, many commonly used clustering methods struggle when clusters have more complex shapes. The aim of n-sphere clustering is to have a method which functions reasonably well, regardless of the shape of the clusters. Accuracy is shown to be low, particularly when cluste

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Sörensen, Kristina. "Clustering in Financial Markets : A Network Theory Approach." Thesis, KTH, Optimeringslära och systemteori, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-150577.

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In this thesis we consider graph partition of a particular kind of complex networks referred to as power law graphs. In particular, we focus our analysis on the market graph, constructed from time series of price return on the American stock market. Two different methods originating from clustering analysis in social networks and image segmentation are applied to obtain graph partitions and the results are evaluated in terms of the structure and quality of the partition. Along with the market graph, power law graphs from three different theoretical graph models are considered. This study highl

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Chakeri, Alireza. "Scalable Clustering Using the Dempster-Shafer Theory of Evidence." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6478.

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Clustering large data sets has become very important as the amount of available unlabeled data increases. Single Pass Fuzzy C-Means (SPFCM) is useful when memory is too limited to load the whole data set. The main idea is to divide dataset into several chunks and to apply fuzzy c-means (FCM) to each chunk. SPFCM uses the weighted cluster centers of the previous chunk in the next data chunks. However, when the number of chunks is increased, the algorithm shows sensitivity to the order in which the data is processed. Hence, we improved SPFCM by recognizing boundary and noisy data in each chunk a

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Chang, Soong Uk. "Clustering with mixed variables /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19086.pdf.

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Xu, Tianbing. "Nonparametric evolutionary clustering." Diss., Online access via UMI:, 2009.

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Narreddy, Naga Sambu Reddy, and Tuğrul Durgun. "Clusters (k) Identification without Triangle Inequality : A newly modelled theory." Thesis, Uppsala universitet, Institutionen för informatik och media, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-183608.

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Cluster analysis characterizes data that are similar enough and useful into meaningful groups (clusters).For example, cluster analysis can be applicable to find group of genes and proteins that are similar, to retrieve information from World Wide Web, and to identify locations that are prone to earthquakes. So the study of clustering has become very important in several fields, which includes psychology and other social sciences, biology, statistics, pattern recognition, information retrieval, machine learning and data mining [1] [2]. Cluster analysis is the one of the widely used technique

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Liu, Ye. "Numerical algorithms for data clustering." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/701.

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Data clustering is a process of grouping unlabeled objects based on the imformation describing their relationship. And it has obtained a lot of attentions in data mining for its wide applications in life. For example, in marketing, companys are interested in ﬁnding groups of customers with similar purchase behavior, which will help them to make suitable plans to gain more proﬁts. Besides, in biology, we can make use of data clustering to distinguish planets and animals given their features. Whats more, in earthquake analysis, by clustering observed earthquake epicenters, dangerous area can be

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Zhang, Kai. "Kernel-based clustering and low rank approximation /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20ZHANG.

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Itoh, Makoto. "Gravitational Clustering of Galaxies:Comparison between Thermodynamic Theory and N-Body Simulations." 京都大学 (Kyoto University), 1990. http://hdl.handle.net/2433/86419.

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Masum, Mohammad. "Vertex Weighted Spectral Clustering." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3266.

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Spectral clustering is often used to partition a data set into a specified number of clusters. Both the unweighted and the vertex-weighted approaches use eigenvectors of the Laplacian matrix of a graph. Our focus is on using vertex-weighted methods to refine clustering of observations. An eigenvector corresponding with the second smallest eigenvalue of the Laplacian matrix of a graph is called a Fiedler vector. Coefficients of a Fiedler vector are used to partition vertices of a given graph into two clusters. A vertex of a graph is classified as unassociated if the Fiedler coefficient of the v

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Books on the topic "Clustering theory"

1

Xu, Rui. Clustering. Wiley, 2009.

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Gutman, S. Root clustering in parameter space. Springer-Verlag, 1990.

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Janowitz, M. F. Ordinal and relational clustering. World Scientific, 2010.

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Meng, Lei, Ah-Hwee Tan, and Donald C. Wunsch II. Adaptive Resonance Theory in Social Media Data Clustering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02985-2.

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Data clustering: Algorithms and applications. Chapman and Hall/CRC, 2014.

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1952-, Basford Kaye E., ed. Mixture models: Inference and applications to clustering. M. Dekker, 1988.

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L, McQuitty Louis. Pattern-analytic clustering: Theory, method, research, and configural findings. University Press of America, 1987.

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Harakeh, M. N. Correlations and Clustering Phenomena in Subatomic Physics. Springer US, 1997.

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9

Introduction to clustering large and high-dimensional data. Cambridge University Press, 2007.

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10

Thurow, Amy Purvis. Toward an augmented theory of cooperative behavior: The case of clustering in animal agriculture. Dept. of Agricultural Economics, Texas A & M University, 1998.

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Book chapters on the topic "Clustering theory"

1

He, Ping, Xiaohua Xu, Lei Zhang, Wei Zhang, Kanwen Li, and Heng Qian. "Constrained Community Clustering." In Intelligent Computing Theory. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09333-8_87.

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Maalel, Wiem, Kuang Zhou, Arnaud Martin, and Zied Elouedi. "Belief Hierarchical Clustering." In Belief Functions: Theory and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11191-9_8.

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Das, Panchanan. "Modelling Volatility Clustering." In Econometrics in Theory and Practice. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9019-8_13.

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Ben-David, Shai, Ulrike von Luxburg, and Dávid Pál. "A Sober Look at Clustering Stability." In Learning Theory. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11776420_4.

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Ben-David, Shai. "A Framework for Statistical Clustering with a Constant Time Approximation Algorithms for K-Median Clustering." In Learning Theory. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27819-1_29.

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Nascimento, Mariá Cristina Vasconcelos, Franklina Maria Bragion de Toledo, and André C. Ponce Leon Ferreira Carvalho. "Consensus Clustering Using Spectral Theory." In Advances in Neuro-Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02490-0_57.

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Hassin, Refael, and Shlomi Rubinstein. "Robust Matchings and Maximum Clustering." In Algorithm Theory - SWAT 2000. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44985-x_23.

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Brautbar, Michael, and Michael Kearns. "A Clustering Coefficient Network Formation Game." In Algorithmic Game Theory. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24829-0_21.

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Clarke, Bertrand, Ernest Fokoué, and Hao Helen Zhang. "Unsupervised Learning: Clustering." In Principles and Theory for Data Mining and Machine Learning. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-98135-2_8.

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Vos, Hans J. "Application of Bayesian Decision Theory to Constrained Classification Networks." In Classification, Clustering, and Data Analysis. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8_20.

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Conference papers on the topic "Clustering theory"

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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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Danko, Jakub, and Tomáš Löster. "Clustering using graph theory tools." In International Days of Statistics and Economics 2019. Libuše Macáková, MELANDRIUM, 2019. http://dx.doi.org/10.18267/pr.2019.los.186.176.

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Wang, Xueen, Deqiang Han, and Chongzhao Han. "Ensemble clustering based on evidence theory." In 2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017. http://dx.doi.org/10.23919/icif.2017.8009726.

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Jafarkhani, Hamid, and Vahid Tarokh. "Image clustering using fuzzy graph theory." In Electronic Imaging, edited by Minerva M. Yeung, Boon-Lock Yeo, and Charles A. Bouman. SPIE, 1999. http://dx.doi.org/10.1117/12.373555.

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Tatikonda, Sekhar. "Clustering phenomena in message-passing schemes." In 2009 IEEE Information Theory Workshop on Networking and Information Theory (ITW). IEEE, 2009. http://dx.doi.org/10.1109/itwnit.2009.5158531.

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Ming Li, Xin Chen, Xin Li, Bin Ma, and P. Vitanyi. "Clustering by compression." In IEEE International Symposium on Information Theory, 2003. Proceedings. IEEE, 2003. http://dx.doi.org/10.1109/isit.2003.1228276.

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Shinkar, Tal, Eitan Yaakobi, Andreas Lenz, and Antonia Wachter-Zeh. "Clustering-Correcting Codes." In 2019 IEEE International Symposium on Information Theory (ISIT). IEEE, 2019. http://dx.doi.org/10.1109/isit.2019.8849737.

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Chan, Chung, Ali Al-Bashabsheh, and Qiaoqiao Zhou. "Agglomerative Info-Clustering." In 2018 IEEE International Symposium on Information Theory (ISIT). IEEE, 2018. http://dx.doi.org/10.1109/isit.2018.8437930.

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Hamad, Denis, and Philippe Biela. "Introduction to spectral clustering." In Communication Technologies: from Theory to Applications (ICTTA). IEEE, 2008. http://dx.doi.org/10.1109/ictta.2008.4529994.

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Khaleghi, Azadeh, and Daniil Ryabko. "Clustering piecewise stationary processes." In 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2020. http://dx.doi.org/10.1109/isit44484.2020.9174045.

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Reports on the topic "Clustering theory"

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Kung, H. T. Clustering Theory and Applications. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada560226.

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Stell, George. Studies in Clustering Theory. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada230967.

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Poggio, Tomaso, and Federico Girosi. Extensions of a Theory of Networks for Approximation and Learning: Dimensionality Reduction and Clustering. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada224517.

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Hrenya, Christine. Quantifying the uncertainty of kinetic-theory predictions of clustering. Final Report covering 21 September 2011 - 20 September 2014. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1169085.

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Kersten, Paul R. Fuzzy Order Statistics and Their Application to Fuzzy Clustering. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada368624.

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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.

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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 algo

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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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Paynter, Robin A., Celia Fiordalisi, Elizabeth Stoeger, et al. A Prospective Comparison of Evidence Synthesis Search Strategies Developed With and Without Text-Mining Tools. Agency for Healthcare Research and Quality (AHRQ), 2021. http://dx.doi.org/10.23970/ahrqepcmethodsprospectivecomparison.

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Background: In an era of explosive growth in biomedical evidence, improving systematic review (SR) search processes is increasingly critical. Text-mining tools (TMTs) are a potentially powerful resource to improve and streamline search strategy development. Two types of TMTs are especially of interest to searchers: word frequency (useful for identifying most used keyword terms, e.g., PubReminer) and clustering (visualizing common themes, e.g., Carrot2). Objectives: The objectives of this study were to compare the benefits and trade-offs of searches with and without the use of TMTs for evidence

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