Relevant bibliographies by topics / Categorical method

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

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 (April 1, 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 (March 8, 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 (May 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 (September 15, 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 (June 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 sequence datasets. Due to the high computational complexity of hierarchical clustering algorithms for clustering large datasets, a new clustering method is required. Therefore, we propose a new scalable clustering method using sampling and a k-nearest-neighbor method. Using a splice dataset and a synthetic dataset, we show that the quality of clusters generated by our proposed approach is better than that of clusters produced by traditional algorithms.
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He, Zengyou, Xiaofei Xu, and Shengchun Deng. "A cluster ensemble method for clustering categorical data." Information Fusion 6, no. 2 (June 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 (September 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 (March 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 (May 12, 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 (November 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. Simulation techniques are then utilized in comparing the stratified Wilcoxon, McCullagh and chi squared-type statistic in their efficiencies at detecting a location shift when the data are examined over two time points. The distribution of a chi squared-type statistic based on the simple contingency table constructed by merely noting whether a subject improved, stayed the same or deteriorated is derived. Applications of these methods and results to a data set of Multiple Sclerosis patients, some of whom were treated with interferon and some of whom received a placebo are provided throughout the thesis and our findings are summarized in the last Chapter.
Science, Faculty of
Statistics, Department of
Graduate
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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 literature for robust design with categorical response variables have various limitations. This study offers a simple and effective method for the analysis of categorical response data for robust product or process design. This method handles both location and dispersion effects to explore robust settings in an effective way. The method is illustrated on two cases: A foam molding process design and an iron-casting process design.
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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.
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, and 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 detection theory, which gives evidence of interindividual variability in how respondents perceive category boundaries and even intraindividual variability in how a respondent perceives category boundaries across trials. Although the measurement of category boundary standard deviations appears to be important for describing the way respondents perceive category boundaries on the latent scale, the inclusion of category boundary standard deviations in the scaling model exposes an inconsistency between the model and the rating method. Namely, with category boundary variability, the scaling model suggests that a respondent could experience disordinal category boundaries on a given trial. However, the idea that a respondent actually experiences disordinal category boundaries seems unlikely. The discrepancy between the assumptions of the scaling model and the way responses are made at the individual level indicates that the assumptions of the model will likely not be met. Therefore, the current work examined how well model parameters could be estimated when the assumptions of the model were violated in various ways as a consequence of disordinal category boundary perceptions. A parameter recovery study examined the effect of model violations on estimation accuracy by comparing estimates obtained from three response processes that violated the assumptions of the model with estimates obtained from a novel response process that did not violate the assumptions of the model. Results suggest all parameters in the Law of Categorical Judgment can be estimated reasonably well when these particular model violations occur, albeit to a lesser degree of accuracy than when the assumptions of the model are met.
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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)
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
color-difference detection thresholds were estimated on the average boundary as well as within the mavi and lacivert categories. The thresholds were also estimated in the green region, in which both groups had only one color term. 2-TAFC method, which eliminates the effects of memory or labeling and isolates the perceptual processes, was used to estimate the thresholds. Turkish speakers, and not non-Turkish speakers, were predicted to show CP effects only in the blue region: thresholds should be lower on the boundary than within-category. The result revealed that Turkish speakers&rsquo
color-difference detection thresholds were lower than those of non-Turkish speakers both in the blue and the green regions. The difference in the green region does not rule out the LRH. It is possible that this difference resulted from the limitations of the study. Finally, in Experiment 3, Turkish speakers&rsquo
thresholds were also estimated on their individual boundaries. The patterns of the thresholds revealed by Experiment 3 were similar to the pattern of the thresholds in Experiment 2.
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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 wealth of theories and applications on the RSS bootstrap that exist nowadays. Moreover, the performance of RSS in resampling is explored. Furthermore, the application of the bootstrap method in the inference of contingency table test is studied. Coefficient of variation: This part shows the capacity of the bootstrap for inferring the coefficient of variation, a task which the asymptotic method does not perform very well. Information theory: There are few works on the study of information theory, especially on the inference of entropy. The papers included in this thesis try to achieve the inference of entropy using the bootstrap method.
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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 exemples numériques sont testés pour comparer les performances de nombreuses techniques d’apprentissage. Sur la base de la représentation d'ordre réduit obtenue par Isomap, les opérateurs de mutation et de croisement évolutifs basés sur les graphes sont proposés pour traiter des problèmes d'optimisation structurelle catégoriels, notamment la conception du dôme, du cadre rigide de six étages et des structures en forme de dame. Ensuite, la méthode de recherche continue consistant à déplacer des asymptotes est exécutée et fournit une solution compétitive, mais inadmissible, en quelques rares itérations. Ensuite, lors de la deuxième étape, une stratégie de recherche discrète est proposée pour rechercher de meilleures solutions basées sur la recherche de voisins. Afin de traiter le cas dans lequel les instances de conception catégorielles sont réparties sur plusieurs variétés, nous proposons une méthode d'apprentissage des variétés k-variétés basée sur l'analyse en composantes principales pondérées
The thesis concentrates on a methodological research on categorical structural optimizationby means of manifold learning. The main difficulty of handling the categorical optimization problems lies in the description of the categorical variables: they are presented in a category and do not have any orders. Thus the treatment of the design space is a key issue. In this thesis, the non-ordinal categorical variables are treated as multi-dimensional discrete variables, thus the dimensionality of corresponding design space becomes high. In order to reduce the dimensionality, the manifold learning techniques are introduced to find the intrinsic dimensionality and map the original design space to a reduced-order space. The mechanisms of both linear and non-linear manifold learning techniques are firstly studied. Then numerical examples are tested to compare the performance of manifold learning techniques mentioned above. It is found that the PCA and MDS can only deal with linear or globally approximately linear cases. Isomap preserves the geodesic distances for non-linear manifold however, its time consuming is the most. LLE preserves the neighbour weights and can yield good results in a short time. KPCA works like a non-linear classifier and we proves why it cannot preserve distances or angles in some cases. Based on the reduced-order representation obtained by Isomap, the graph-based evolutionary crossover and mutation operators are proposed to deal with categorical structural optimization problems, including the design of dome, six-story rigid frame and dame-like structures. The results show that the proposed graph-based evolutionary approach constructed on the reduced-order space performs more efficiently than traditional methods including simplex approach or evolutionary approach without reduced-order space. In chapter 5, the LLE is applied to reduce the data dimensionality and a polynomial interpolation helps to construct the responding surface from lower dimensional representation to original data. Then the continuous search method of moving asymptotes is executed and yields a competitively good but inadmissible solution within only a few of iteration numbers. Then in the second stage, a discrete search strategy is proposed to find out better solutions based on a neighbour search. The ten-bar truss and dome structural design problems are tested to show the validity of the method. In the end, this method is compared to the Simulated Annealing algorithm and Covariance Matrix Adaptation Evolutionary Strategy, showing its better optimization efficiency. In chapter 6, in order to deal with the case in which the categorical design instances are distributed on several manifolds, we propose a k-manifolds learning method based on the Weighted Principal Component Analysis. And the obtained manifolds are integrated in the lower dimensional design space. Then the method introduced in chapter 4 is applied to solve the ten-bar truss, the dome and the dame-like structural design problems
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Books on the topic "Categorical method"

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

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

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

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

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

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Cordier, J. M. Shape theory: Categorical methods of approximation. Chichester, West Sussex, England: Ellis Horwood, 1989.

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

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Yang, Keming, ed. Categorical Data Analysis. Los Angeles, USA: SAGE Publications Ltd, 2014.

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Institute, SAS, ed. Visualizing categorical data. Cary, NC: 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. Providence, Rhode Island: 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, 112–26. Cham: 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, 215–35. Boston, MA: 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, 91–99. Berlin, Heidelberg: 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, 282–93. Berlin, Heidelberg: 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, 319–38. Cham: 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, 223–38. Cham: 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, 736–44. Cham: 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, 602–11. Berlin, Heidelberg: 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, 228–42. Berlin, Heidelberg: 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, 389–96. Heidelberg: 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. New York, NY, USA: 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. New York, NY, USA: 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. New York, NY, USA: 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, September 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 provide guidance on fitting FIML models for nonresponse missing at random (MAR) using the methods of Fuchs (1982) and Fay (1986), who incorporated item nonresponse indicators within a structural modeling framework. We compare both FIML for MAR and FIML for MNAR nonresponse models for independent and dependent variables. Also, we provide recommendations for future applications of FIML using Latent GOLD.
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