Relevant bibliographies by topics / Categorical method

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  2. Dissertations / Theses
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Academic literature on the topic 'Categorical method'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Categorical method"

1

Oh, Seung-Joon, and Jae-Yearn Kim. "A Scalable Clustering Method for Categorical Sequences." Journal of Korean Institute of Intelligent Systems 14, no. 2 (2004): 136–41. http://dx.doi.org/10.5391/jkiis.2004.14.2.136.

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Giordan, Marco, and Giancarlo Diana. "A Clustering Method for Categorical Ordinal Data." Communications in Statistics - Theory and Methods 40, no. 7 (2011): 1315–34. http://dx.doi.org/10.1080/03610920903581010.

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Baba, Yasumasa. "Graphical prediction method based on categorical data." Computational Statistics & Data Analysis 5, no. 2 (1987): 85–101. http://dx.doi.org/10.1016/0167-9473(87)90034-x.

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Seman, Ali, Zainab Abu Bakar, Azizian Mohd. Sapa, and Ida Rosmini Othman. "A Medoid-based Method for Clustering Categorical Data." Journal of Artificial Intelligence 6, no. 4 (2013): 257–65. http://dx.doi.org/10.3923/jai.2013.257.265.

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OH, SEUNG-JOON, and JAE-YEARN KIM. "A SCALABLE CLUSTERING METHOD FOR CATEGORICAL SEQUENCE DATA." International Journal of Computational Methods 02, no. 02 (2005): 167–80. http://dx.doi.org/10.1142/s0219876205000417.

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Clustering of sequences is relatively less explored but it is becoming increasingly important in data mining applications such as web usage mining and bioinformatics. The web user segmentation problem uses web access log files to partition a set of users into clusters such that users within one cluster are more similar to one another than to the users in other clusters. Similarly, grouping protein sequences that share a similar structure can help to identify sequences with similar functions. However, few clustering algorithms consider sequentiality. In this paper, we study how to cluster seque
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He, Zengyou, Xiaofei Xu, and Shengchun Deng. "A cluster ensemble method for clustering categorical data." Information Fusion 6, no. 2 (2005): 143–51. http://dx.doi.org/10.1016/j.inffus.2004.03.001.

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Cao, Fuyuan, Jiye Liang, and Liang Bai. "A new initialization method for categorical data clustering." Expert Systems with Applications 36, no. 7 (2009): 10223–28. http://dx.doi.org/10.1016/j.eswa.2009.01.060.

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Cao, Fuyuan, and Jiye Liang. "A data labeling method for clustering categorical data." Expert Systems with Applications 38, no. 3 (2011): 2381–85. http://dx.doi.org/10.1016/j.eswa.2010.08.026.

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Hargrove, William W., Forrest M. Hoffman, and Paul F. Hessburg. "Mapcurves: a quantitative method for comparing categorical maps." Journal of Geographical Systems 8, no. 2 (2006): 187–208. http://dx.doi.org/10.1007/s10109-006-0025-x.

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Moskaliuk, S. S. "Method of categorical extension of Cayley-Klein groups." Czechoslovak Journal of Physics 55, no. 11 (2005): 1495–501. http://dx.doi.org/10.1007/s10582-006-0031-8.

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Dissertations / Theses on the topic "Categorical method"

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Chang, Janis. "Analysis of ordered categorical data." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/27857.

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Methods of testing for a location shift between two populations in a longitudinal study are investigated when the data of interest are ordered, categorical and non-linear. A non-standard analysis involving modelling of data over time with transition probability matrices is discussed. Next, the relative efficiencies of statistics more frequently used for the analysis of such categorical data at a single time point are examined. The Wilcoxon rank sum, McCullagh, and 2 sample t statistic are compared for the analysis of such cross sectional data using simulation and efficacy calculations. Simulat
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Erdural, Serkan. "A Method For Robust Design Of Products Or Processes With Categorical Response." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/3/12608015/index.pdf.

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In industrial processes decreasing variation is very important while achieving the targets. For manufacturers, finding out optimal settings of product and process parameters that are capable of producing desired results under great conditions is crucial. In most cases, the quality response is measured on a continuous scale. However, in some cases, the desired quality response may be qualitative (categorical). There are many effective methods to design robust products/process through industrial experimentation when the response variable is continuous. But methods proposed so far in the literatu
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Kjellsson, Maria C. "Methodological Studies on Models and Methods for Mixed-Effects Categorical Data Analysis." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-9333.

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Chantarat, Navara. "Modern design of experiments methods for screening and experimentations with mixture and qualitative variables." Columbus, OH : Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1064198056.

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Thesis (Ph. D.)--Ohio State University, 2003.<br>Title from first page of PDF file. Document formatted into pages; contains xiv, 119 p.: ill. (some col.). Includes abstract and vita. Advisor: Theodore T. Allen, Dept. of Industrial and Systems Engineering. Includes bibliographical references (p. 111-119).
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鈴木, 郁子, Ikuko SUZUKI, 真雄 和田, Shinyu WADA, 隆. 村上 та Takashi MURAKAMI. "KJ法および多重対応分析を用いた自由記述型応答の数量化". 名古屋大学大学院教育発達科学研究科, 2005. http://hdl.handle.net/2237/9441.

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King, David R. "A bayesian solution for the law of categorical judgment with category boundary variability and examination of robustness to model violations." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52960.

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Previous solutions for the the Law of Categorical Judgment with category boundary variability have either constrained the standard deviations of the category boundaries in some way or have violated the assumptions of the scaling model. In the current work, a fully Bayesian Markov chain Monte Carlo solution for the Law of Categorical Judgment is given that estimates all model parameters (i.e. scale values, category boundaries, and the associated standard deviations). The importance of measuring category boundary standard deviations is discussed in the context of previous research in signal dete
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Kadihasanoglu, Didem. "A Cross-cultural Study On Color Perception: Comparing Turkish And Non-turkish Speakers&#039." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608556/index.pdf.

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Turkish speakers differentiate the blue region of color spectrum into mavi (blue) and lacivert (dark blue)<br>whereas non-Turkish speakers in this study had only one color term in the blue region. The present study aimed to explore the predictions of the Linguistic Relativity Hypothesis. Operationally, Categorical Perception (CP) effects were used. In Experiment 1, Turkish speakers performed a naming task to determine an average category boundary between mavi and lacivert. In Experiment 2, both Turkish and non-Turkish speakers&rsquo<br>color-difference detection thresholds were estimated on th
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Shrimpton, John. "Graphs, symmetry and categorical methods." Thesis, Bangor University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235884.

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Amiri, Saeid. "On the Application of the Bootstrap : Coefficient of Variation, Contingency Table, Information Theory and Ranked Set Sampling." Doctoral thesis, Uppsala universitet, Matematiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-159206.

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This thesis deals with the bootstrap method. Three decades after the seminal paper by Bradly Efron, still the horizons of this method need more exploration. The research presented herein has stepped into different fields of statistics where the bootstrap method can be utilized as a fundamental statistical tool in almost any application. The thesis considers various statistical problems, which is explained briefly below. Bootstrap method: A comparison of the parametric and the nonparametric bootstrap of variance is presented. The bootstrap of ranked set sampling is dealt with, as well as the we
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Gao, Huanhuan. "Categorical structural optimization : methods and applications." Thesis, Compiègne, 2019. http://www.theses.fr/2019COMP2471/document.

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La thèse se concentre sur une recherche méthodologique sur l'optimisation structurelle catégorielle au moyen d'un apprentissage multiple. Dans cette thèse, les variables catégorielles non ordinales sont traitées comme des variables discrètes multidimensionnelles. Afin de réduire la dimensionnalité, les nombreuses techniques d'apprentissage sont introduites pour trouver la dimensionnalité intrinsèque et mapper l'espace de conception d'origine sur un espace d'ordre réduit. Les mécanismes des techniques d'apprentissage à la fois linéaires et non linéaires sont d'abord étudiés. Ensuite, des exempl
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Books on the topic "Categorical method"

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Heijden, Peter van der. Correspondence analysis on longitudinal categorical data. DSWO Press, 1987.

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P. G. M. van der Heijden. Correspondence analysis of longitudinal categorical data. DSWO Press, 1987.

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Categorical Longitudinal Data: Log-linear panel, trend, and cohort analysis. Sage Publications, 1990.

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Nel, L. D. Introduction to categorical methods. Carleton University, 1992.

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Nel, L. D. Introduction to categorical methods. Carleton University, 1991.

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Cordier, J. M. Shape theory: Categorical methods of approximation. Ellis Horwood, 1989.

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1947-, Porter T., ed. Shape theory: Categorical methods of approximation. Dover Publications, 2008.

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Yang, Keming, ed. Categorical Data Analysis. SAGE Publications Ltd, 2014.

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Institute, SAS, ed. Visualizing categorical data. SAS Institute, 2001.

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Srivastava, Ashish, André Leroy, Ivo Herzog, and Pedro Guil Asensio, eds. Categorical, Homological and Combinatorial Methods in Algebra. American Mathematical Society, 2020. http://dx.doi.org/10.1090/conm/751.

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Book chapters on the topic "Categorical method"

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Crăciunean, Daniel-Cristian, and Dimitris Karagiannis. "Categorical Modeling Method of Intelligent WorkFlow." In Mining Intelligence and Knowledge Exploration. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05918-7_11.

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Nair, Vijayan N. "Testing in Industrial Experiments with Ordered Categorical Data." In Quality Control, Robust Design, and the Taguchi Method. Springer US, 1989. http://dx.doi.org/10.1007/978-1-4684-1472-1_11.

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Chavent, Marie, Vanessa Kuentz, and Jérôme Saracco. "A Partitioning Method for the Clustering of Categorical Variables." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10745-0_9.

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Morzy, Tadeusz, Marek Wojciechowski, and Maciej Zakrzewicz. "Scalable Hierarchical Clustering Method for Sequences of Categorical Values." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45357-1_31.

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Huot, Mathieu, Sam Staton, and Matthijs Vákár. "Correctness of Automatic Differentiation via Diffeologies and Categorical Gluing." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45231-5_17.

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AbstractWe present semantic correctness proofs of Automatic Differentiation (AD). We consider a forward-mode AD method on a higher order language with algebraic data types, and we characterise it as the unique structure preserving macro given a choice of derivatives for basic operations. We describe a rich semantics for differentiable programming, based on diffeological spaces. We show that it interprets our language, and we phrase what it means for the AD method to be correct with respect to this semantics. We show that our characterisation of AD gives rise to an elegant semantic proof of its correctness based on a gluing construction on diffeological spaces. We explain how this is, in essence, a logical relations argument. Finally, we sketch how the analysis extends to other AD methods by considering a continuation-based method.
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Yang, Xiaochen, Mingzhi Dong, Yiwen Guo, and Jing-Hao Xue. "Metric Learning for Categorical and Ambiguous Features: An Adversarial Method." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67661-2_14.

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Li, Dong, Huifeng Xue, Wenyu Zhang, and Yan Zhang. "Categorical Data Clustering Method Based on Improved Fruit Fly Optimization Algorithm." In Advances in Intelligent, Interactive Systems and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02804-6_96.

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Martínez, Sergio, Aida Valls, and David Sánchez. "Anonymizing Categorical Data with a Recoding Method Based on Semantic Similarity." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14058-7_62.

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Boullé, Marc. "A Grouping Method for Categorical Attributes Having Very Large Number of Values." In Machine Learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11510888_23.

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Bougeard, Stéphanie, El Mostafa Qannari, and Claire Chauvin. "Multiblock Method for Categorical Variables. Application to the Study of Antibiotic Resistance." In Proceedings of COMPSTAT'2010. Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2604-3_36.

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Conference papers on the topic "Categorical method"

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Zhang, Siyao. "Shrinkage Method for Categorical Explanatory Variables." In ICAIP 2020: 2020 4th International Conference on Advances in Image Processing. ACM, 2020. http://dx.doi.org/10.1145/3441250.3441275.

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Izakian, Hesam, Ajith Abraham, and Vaclav Snasel. "Clustering categorical data using a swarm-based method." In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC 2009). IEEE, 2009. http://dx.doi.org/10.1109/nabic.2009.5393623.

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Jodoin, E., C. A. Pena Reyes, and E. Sanchez. "A Method for the Fuzzification of Categorical Variables." In 2006 IEEE International Conference on Fuzzy Systems. IEEE, 2006. http://dx.doi.org/10.1109/fuzzy.2006.1681807.

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Renard, P., C. Jäggli, Y. Dagasan, and J. Straubhaar. "The Posterior Population Expansion Ensemble Method to Invert Categorical Fields." In Petroleum Geostatistics 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201902270.

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Reddy, H. Venkateswara, and S. Viswanadha Raju. "A Roughset Based Data Labeling Method for Clustering Categorical Data." In 2014 3rd International Conference on Eco-friendly Computing and Communication Systems (ICECCS). IEEE, 2014. http://dx.doi.org/10.1109/eco-friendly.2014.86.

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Chen, Lifei, Gongde Guo, Shengrui Wang, and Xiangzeng Kong. "Kernel learning method for distance-based classification of categorical data." In 2014 14th UK Workshop on Computational Intelligence (UKCI). IEEE, 2014. http://dx.doi.org/10.1109/ukci.2014.6930159.

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He, Liang, Chao Shen, and Yun Li. "A conditional-probability zone transformation coding method for categorical features." In ACM TURC 2019: ACM Turing Celebration Conference - China. ACM, 2019. http://dx.doi.org/10.1145/3321408.3326636.

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Sreenivasulu, G., S. Viswanadha Raju, and N. Sambasiva Rao. "Data Labeling method based on Rough Entropy for categorical data clustering." In 2014 International Conference on Electronics,Communication and Computational Engineering (ICECCE). IEEE, 2014. http://dx.doi.org/10.1109/icecce.2014.7086654.

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Menezes, Alice A. F., and Carlos M. S. Figueiredo. "A ranking method for location-based categorical data in smart cities." In WebMedia '19: Brazilian Symposium on Multimedia and the Web. ACM, 2019. http://dx.doi.org/10.1145/3323503.3360291.

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Crăciunean, Daniel-Cristian. "Categorical Modeling Method, Proof of Concept for the Petri Net Language." In 7th International Conference on Model-Driven Engineering and Software Development. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007360602810289.

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Reports on the topic "Categorical method"

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Edwards, Susan L., Marcus E. Berzofsky, and Paul P. Biemer. Addressing Nonresponse for Categorical Data Items Using Full Information Maximum Likelihood with Latent GOLD 5.0. RTI Press, 2018. http://dx.doi.org/10.3768/rtipress.2018.mr.0038.1809.

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Full information maximum likelihood (FIML) is an important approach to compensating for nonresponse in data analysis. Unfortunately, only a few software packages implement FIML and even fewer have the capability to compensate for missing not at random (MNAR) nonresponse. One of these packages is Statistical Innovations’ Latent GOLD; however, the user documentation for Latent GOLD provides no mention of this capability. The purpose of this paper is to provide guidance for fitting MNAR FIML models for categorical data items using the Latent GOLD 5.0 software. By way of comparison, we also provid
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