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Journal articles on the topic 'Categorical method'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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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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2

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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3

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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4

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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6

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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9

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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10

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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Naouali, Sami, Semeh Ben Salem, and Zied Chtourou. "Clustering Categorical Data: A Survey." International Journal of Information Technology & Decision Making 19, no. 01 (2020): 49–96. http://dx.doi.org/10.1142/s0219622019300064.

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Clustering is a complex unsupervised method used to group most similar observations of a given dataset within the same cluster. To guarantee high efficiency, the clustering process should ensure high accuracy and low complexity. Many clustering methods were developed in various fields depending on the type of application and the data type considered. Categorical clustering considers segmenting a dataset in which the data are categorical and were widely used in many real-world applications. Thus several methods were developed including hard, fuzzy and rough set-based methods. In this survey, mo

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Dirisinapu, Lakshmi Sreenivasareddy, Krishna Murthy Mudumbi, and Govardhan Aliseri. "Outlier Analysis of Categorical Data Using Infrequency." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 8, no. 3 (2013): 868–73. http://dx.doi.org/10.24297/ijct.v8i3.3397.

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Anomalies are those objects, which will act with different behavior and do not follow with the remaining records in the databases. Detecting anomalies is an important issue in many fields. Though many methods are available to detect anomalies in numerical datasets, only a few methods are available for categorical datasets. In this work, a new method has been proposed. This algorithm finds anomalies based on infrequent itemsets in each record. These outliers are generated by Apriori property on each record values in datasets. Previous methods may not distinguish different records with the same

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13

Onoghojobi, B. "Hill Climbing Method Using Claus Model for Categorical Data." Journal of Mathematics and Statistics 5, no. 4 (2009): 375–78. http://dx.doi.org/10.3844/jmssp.2009.375.378.

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Gubich, Olga V., and Konstantin V. Zashcholkin. "MODIFICATION OF CATEGORICAL-ZONE METHOD FOR DIGITAL WATERMARKS EMBEDDING." ELECTRICAL AND COMPUTER SYSTEMS 19, no. 95 (2015): 262–65. http://dx.doi.org/10.15276/eltecs.19.95.2015.58.

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15

Yoon, Yong-Hwa, and Bo-Seung Choi. "Model selection method for categorical data with non-response." Journal of the Korean Data and Information Science Society 23, no. 4 (2012): 627–41. http://dx.doi.org/10.7465/jkdi.2012.23.4.627.

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16

Timofeyeva, Galina Adolfovna, and Dmitry Vladimirovich Bondarchuk. "Mathematical Foundations of categorical vector method in data mining." Herald of the Ural State University of Railway Transport, no. 4 (2015): 4–8. http://dx.doi.org/10.20291/2079-0392-2015-4-4-8.

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17

Попей-олл, С., and S. Popey-oll. "The Theory of Self-Identification and Categorical Research Method." Scientific Research and Development. Socio-Humanitarian Research and Technology 8, no. 1 (2019): 17–25. http://dx.doi.org/10.12737/article_5c8f49fbadbbc6.99681771.

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This article presents a categorical method for analyzing the complex processes of personal identity. Human experiences are a result of conscious generalizations that dominate culture and are fixed in semantic categories. The rapid transformation of society fragments a life into many identifying parameters. Therefore, «a self-concept» and a semantic category of being may not be consistent with each other. The harmonious level of self-organization is manifested in the sensory coherence of people: an intention and an expectation. And fragmentation is a chaos of self-awareness and loss of an emoti

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18

Cheng, Li, Yijie Wang, and Xingkong Ma. "A Neural Probabilistic outlier detection method for categorical data." Neurocomputing 365 (November 2019): 325–35. http://dx.doi.org/10.1016/j.neucom.2019.07.069.

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19

Rezaee, Hassan, and Denis Marcotte. "Calibration of categorical simulations by evolutionary gradual deformation method." Computational Geosciences 22, no. 2 (2018): 587–605. http://dx.doi.org/10.1007/s10596-017-9711-7.

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te Grotenhuis, Manfred, Ben Pelzer, Rob Eisinga, Rense Nieuwenhuis, Alexander Schmidt-Catran, and Ruben Konig. "A novel method for modelling interaction between categorical variables." International Journal of Public Health 62, no. 3 (2016): 427–31. http://dx.doi.org/10.1007/s00038-016-0902-0.

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21

Bai, Liang, Jiye Liang, Chuangyin Dang, and Fuyuan Cao. "A cluster centers initialization method for clustering categorical data." Expert Systems with Applications 39, no. 9 (2012): 8022–29. http://dx.doi.org/10.1016/j.eswa.2012.01.131.

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22

Johnson, J. Michael, and Keith C. Clarke. "An area preserving method for improved categorical raster resampling." Cartography and Geographic Information Science 48, no. 4 (2021): 292–304. http://dx.doi.org/10.1080/15230406.2021.1892531.

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23

Kostek, Bożena, Piotr Odya, and Piotr Suchomski. "Loudness Scaling Test Based on Categorical Perception." Archives of Acoustics 41, no. 4 (2016): 637–48. http://dx.doi.org/10.1515/aoa-2016-0061.

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Abstract The main goal of this research study is focused on creating a method for loudness scaling based on categorical perception. Its main features, such as: way of testing, calibration procedure for securing reliable results, employing natural test stimuli, etc., are described in the paper and assessed against a procedure that uses 1/2-octave bands of noise (LGOB) for the loudness growth estimation. The Mann-Whitney U-test is employed to check whether the proposed method is statistically equivalent to LGOB. It is shown that loudness functions obtained in both methods are similar in the stat

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24

von Eye, Alexander, Christof Schuster, and William M. Rogers. "Modelling Synergy using Manifest Categorical Variables." International Journal of Behavioral Development 22, no. 3 (1998): 537–57. http://dx.doi.org/10.1080/016502598384261.

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This paper discusses methods to model the concept of synergy at the level of manifest categorical variables. First, a classification of concepts of synergy is presented. A dditive and nonadditive concepts of synergy are distinguished. Most prominent among the nonadditive concepts is superadditive synergy. Examples are given from the natural sciences and the social sciences. M delling focuses on the relationship between the agents involved in a synergetic process. These relationships are expressed in form of contrasts, expressed in effect coding vectors in design matrices for nonstandard log-li

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25

Yang, Yanyun, and Yan Xia. "Categorical Omega With Small Sample Sizes via Bayesian Estimation: An Alternative to Frequentist Estimators." Educational and Psychological Measurement 79, no. 1 (2018): 19–39. http://dx.doi.org/10.1177/0013164417752008.

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When item scores are ordered categorical, categorical omega can be computed based on the parameter estimates from a factor analysis model using frequentist estimators such as diagonally weighted least squares. When the sample size is relatively small and thresholds are different across items, using diagonally weighted least squares can yield a substantially biased estimate of categorical omega. In this study, we applied Bayesian estimation methods for computing categorical omega. The simulation study investigated the performance of categorical omega under a variety of conditions through manipu

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26

Wu, Chengyuan, and Carol Anne Hargreaves. "Topological Machine Learning for Mixed Numeric and Categorical Data." International Journal on Artificial Intelligence Tools 30, no. 05 (2021): 2150025. http://dx.doi.org/10.1142/s0218213021500251.

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Topological data analysis is a relatively new branch of machine learning that excels in studying high-dimensional data, and is theoretically known to be robust against noise. Meanwhile, data objects with mixed numeric and categorical attributes are ubiquitous in real-world applications. However, topological methods are usually applied to point cloud data, and to the best of our knowledge there is no available framework for the classification of mixed data using topological methods. In this paper, we propose a novel topological machine learning method for mixed data classification. In the propo

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27

Nguyen, Dang, Sunil Gupta, Santu Rana, Alistair Shilton, and Svetha Venkatesh. "Bayesian Optimization for Categorical and Category-Specific Continuous Inputs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5256–63. http://dx.doi.org/10.1609/aaai.v34i04.5971.

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Many real-world functions are defined over both categorical and category-specific continuous variables and thus cannot be optimized by traditional Bayesian optimization (BO) methods. To optimize such functions, we propose a new method that formulates the problem as a multi-armed bandit problem, wherein each category corresponds to an arm with its reward distribution centered around the optimum of the objective function in continuous variables. Our goal is to identify the best arm and the maximizer of the corresponding continuous function simultaneously. Our algorithm uses a Thompson sampling s

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28

Blackman, Sherry. "Comparisons among Methods of Scoring Androgyny Continuously Using Computer-Simulated Data." Psychological Reports 57, no. 1 (1985): 151–54. http://dx.doi.org/10.2466/pr0.1985.57.1.151.

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To study the relationship between the Bem-Spence categorical method of scoring androgyny and a number of continuous methods, Bem's Masculinity and Femininity scores were simulated for 50 studies of 200 subjects each. The continuous methods relate closely to one another and leave between 64% and 71% of the variance in the categorical method unaccounted for. There is little evidence for choosing one method over another. Research is needed to compare the predictions of the median-split method with those of the continuous methods.

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29

Sanchez, Jeniffer Duarte, Leandro C. Rêgo, and Raydonal Ospina. "Prediction by Empirical Similarity via Categorical Regressors." Machine Learning and Knowledge Extraction 1, no. 2 (2019): 641–52. http://dx.doi.org/10.3390/make1020038.

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A quantifier of similarity is generally a type of score that assigns a numerical value to a pair of sequences based on their proximity. Similarity measures play an important role in prediction problems with many applications, such as statistical learning, data mining, biostatistics, finance and others. Based on observed data, where a response variable of interest is assumed to be associated with some regressors, it is possible to make response predictions using a weighted average of observed response variables, where the weights depend on the similarity of the regressors. In this work, we prop

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30

Kondo, Tadafumi, and Yuchi Kanzawa. "Fuzzy Clustering Methods for Categorical Multivariate Data Based on q-Divergence." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 4 (2018): 524–36. http://dx.doi.org/10.20965/jaciii.2018.p0524.

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This paper presents two fuzzy clustering algorithms for categorical multivariate data based on q-divergence. First, this study shows that a conventional method for vectorial data can be explained as regularizing another conventional method using q-divergence. Second, based on the known results that Kullback-Leibler (KL)-divergence is generalized into the q-divergence, and two conventional fuzzy clustering methods for categorical multivariate data adopt KL-divergence, two fuzzy clustering algorithms for categorical multivariate data that are based on q-divergence are derived from two optimizati

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31

ADACHI, Kohei, and Hiroshi TANAKA. "A Method for Scaling Categorical Attributes with Inter-Objec dissimilarity." Kodo Keiryogaku (The Japanese Journal of Behaviormetrics) 22, no. 2 (1995): 110–25. http://dx.doi.org/10.2333/jbhmk.22.110.

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32

Thomas, Roy. "A Novel Ensemble Method for Detecting Outliers in Categorical Data." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 4 (2020): 4947–53. http://dx.doi.org/10.30534/ijatcse/2020/108942020.

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33

Geng, Zhi, Yang-Bo He, Xue-Li Wang, and Qiang Zhao. "Bayesian method for learning graphical models with incompletely categorical data." Computational Statistics & Data Analysis 44, no. 1-2 (2003): 175–92. http://dx.doi.org/10.1016/s0167-9473(03)00066-5.

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34

Choi yun-hi. "Writing Method and Categorical Variation for Hangul Titles with GeanMoonLok." Korean Classical Woman Literature Studies ll, no. 17 (2008): 413–38. http://dx.doi.org/10.17090/kcwls.2008..17.413.

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35

De Angelis, Luca, and José G. Dias. "Mining categorical sequences from data using a hybrid clustering method." European Journal of Operational Research 234, no. 3 (2014): 720–30. http://dx.doi.org/10.1016/j.ejor.2013.11.002.

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36

Dovbysh, A. S., V. V. Moskalenko, and A. S. Rizhova. "Information-Extreme Method for Classification of Observations with Categorical Attributes." Cybernetics and Systems Analysis 52, no. 2 (2016): 224–31. http://dx.doi.org/10.1007/s10559-016-9818-1.

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37

Yuan, Liang, Wenjian Wang, and Lifei Chen. "Two-stage pruning method for gram-based categorical sequence clustering." International Journal of Machine Learning and Cybernetics 10, no. 4 (2017): 631–40. http://dx.doi.org/10.1007/s13042-017-0744-y.

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38

Bondar, Yulia. "CATEGORICAL DISTINCTION OF THE CONCEPTS “INTERACTIVE TECHNOLOGY” AND “INTERACTIVE METHOD”." Knowledge, Education, Law, Management 1, no. 4 (2020): 8–13. http://dx.doi.org/10.51647/kelm.2020.4.1.2.

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39

Ji, Jinchao, Wei Pang, Yanlin Zheng, Zhe Wang, and Zhiqiang Ma. "An Initialization Method for Clustering Mixed Numeric and Categorical Data Based on the Density and Distance." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 07 (2015): 1550024. http://dx.doi.org/10.1142/s021800141550024x.

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Most of the initialization approaches are dedicated to the partitional clustering algorithms which process categorical or numerical data only. However, in real-world applications, data objects with both numeric and categorical features are ubiquitous. The coexistence of both categorical and numerical attributes make the initialization methods designed for single-type data inapplicable to mixed-type data. Furthermore, to the best of our knowledge, in the existing partitional clustering algorithms designed for mixed-type data, the initial cluster centers are determined randomly. In this paper, w

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40

Kumar, Ajay, and Shishir Kumar. "A Support Based Initialization Algorithm for Categorical Data Clustering." Journal of Information Technology Research 11, no. 2 (2018): 53–67. http://dx.doi.org/10.4018/jitr.2018040104.

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Several initial center selection algorithms are proposed in the literature for numerical data, but the values of the categorical data are unordered so, these methods are not applicable to a categorical data set. This article investigates the initial center selection process for the categorical data and after that present a new support based initial center selection algorithm. The proposed algorithm measures the weight of unique data points of an attribute with the help of support and then integrates these weights along the rows, to get the support of every row. Further, a data object having th

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41

Alkharusi, Hussain. "Categorical Variables in Regression Analysis: A Comparison of Dummy and Effect Coding." International Journal of Education 4, no. 2 (2012): 202. http://dx.doi.org/10.5296/ije.v4i2.1962.

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The use of categorical variables in regression involves the application of coding methods. The purpose of this paper is to describe how categorical independent variables can be incorporated into regression by virtue of two coding methods: dummy and effect coding. The paper discusses the uses, interpretations, and underlying assumptions of each method. In general, overall results of the regression are unaffected by the methods used for coding the categorical independent variables. In any of the methods, the analysis tests whether group membership is related to the dependent variables. Both meth

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42

Hao, Zengchao, Fanghua Hao, Youlong Xia, et al. "A Statistical Method for Categorical Drought Prediction Based on NLDAS-2." Journal of Applied Meteorology and Climatology 55, no. 4 (2016): 1049–61. http://dx.doi.org/10.1175/jamc-d-15-0200.1.

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AbstractDrought is a slowly varying natural phenomenon and may have wide impacts on a range of sectors. Tremendous efforts have therefore been devoted to drought monitoring and prediction to reduce potential impacts of drought. Reliable drought prediction is critically important to provide information ahead of time for early warning to facilitate drought-preparedness plans. The U.S. Drought Monitor (USDM) is a composite drought product that depicts drought conditions in categorical forms, and it has been widely used to track drought and its impacts for operational and research purposes. The US

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43

Nguyen, Huu Hiep. "Clustering Categorical Data Using Community Detection Techniques." Computational Intelligence and Neuroscience 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/8986360.

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With the advent of the k-modes algorithm, the toolbox for clustering categorical data has an efficient tool that scales linearly in the number of data items. However, random initialization of cluster centers in k-modes makes it hard to reach a good clustering without resorting to many trials. Recently proposed methods for better initialization are deterministic and reduce the clustering cost considerably. A variety of initialization methods differ in how the heuristics chooses the set of initial centers. In this paper, we address the clustering problem for categorical data from the perspective

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44

Ahmad, Amir, and Lipika Dey. "A method to compute distance between two categorical values of same attribute in unsupervised learning for categorical data set." Pattern Recognition Letters 28, no. 1 (2007): 110–18. http://dx.doi.org/10.1016/j.patrec.2006.06.006.

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45

Kim, Jihyeok, Reinald Kim Amplayo, Kyungjae Lee, Sua Sung, Minji Seo, and Seung-won Hwang. "Categorical Metadata Representation for Customized Text Classification." Transactions of the Association for Computational Linguistics 7 (November 2019): 201–15. http://dx.doi.org/10.1162/tacl_a_00263.

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The performance of text classification has improved tremendously using intelligently engineered neural-based models, especially those injecting categorical metadata as additional information, e.g., using user/product information for sentiment classification. This information has been used to modify parts of the model (e.g., word embeddings, attention mechanisms) such that results can be customized according to the metadata. We observe that current representation methods for categorical metadata, which are devised for human consumption, are not as effective as claimed in popular classification

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46

Chrisinta, Debora, I. Made Sumertajaya, and Indahwati Indahwati. "EVALUASI KINERJA METODE CLUSTER ENSEMBLE DAN LATENT CLASS CLUSTERING PADA PEUBAH CAMPURAN." Indonesian Journal of Statistics and Its Applications 4, no. 3 (2020): 448–61. http://dx.doi.org/10.29244/ijsa.v4i3.630.

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Most of the traditional clustering algorithms are designed to focus either on numeric data or on categorical data. The collected data in the real-world often contain both numeric and categorical attributes. It is difficult for applying traditional clustering algorithms directly to these kinds of data. So, the paper aims to show the best method based on the cluster ensemble and latent class clustering approach for mixed data. Cluster ensemble is a method to combine different clustering results from two sub-datasets: the categorical and numerical variables. Then, clustering algorithms are design

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47

Chen, Han-Ching, and Nae-Sheng Wang. "The Assignment of Scores Procedure for Ordinal Categorical Data." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/304213.

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Ordinal data are the most frequently encountered type of data in the social sciences. Many statistical methods can be used to process such data. One common method is to assign scores to the data, convert them into interval data, and further perform statistical analysis. There are several authors who have recently developed assigning score methods to assign scores to ordered categorical data. This paper proposes an approach that defines an assigning score system for an ordinal categorical variable based on underlying continuous latent distribution with interpretation by using three case study e

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48

Lee, Changki, and Uk Jung. "Context-Based Geodesic Dissimilarity Measure for Clustering Categorical Data." Applied Sciences 11, no. 18 (2021): 8416. http://dx.doi.org/10.3390/app11188416.

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Measuring the dissimilarity between two observations is the basis of many data mining and machine learning algorithms, and its effectiveness has a significant impact on learning outcomes. The dissimilarity or distance computation has been a manageable problem for continuous data because many numerical operations can be successfully applied. However, unlike continuous data, defining a dissimilarity between pairs of observations with categorical variables is not straightforward. This study proposes a new method to measure the dissimilarity between two categorical observations, called a context-b

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Dong, Bin, Songlei Jian, and Ke Zuo. "CDE++: Learning Categorical Data Embedding by Enhancing Heterogeneous Feature Value Coupling Relationships." Entropy 22, no. 4 (2020): 391. http://dx.doi.org/10.3390/e22040391.

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Categorical data are ubiquitous in machine learning tasks, and the representation of categorical data plays an important role in the learning performance. The heterogeneous coupling relationships between features and feature values reflect the characteristics of the real-world categorical data which need to be captured in the representations. The paper proposes an enhanced categorical data embedding method, i.e., CDE++, which captures the heterogeneous feature value coupling relationships into the representations. Based on information theory and the hierarchical couplings defined in our previo

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50

Arengas, Gustavo. "Categorical definitions and properties via generators." Revista Colombiana de Matemáticas 53, no. 2 (2019): 165–84. http://dx.doi.org/10.15446/recolma.v53n2.85525.

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In the present work, we show how the study of categorical constructions does not have to be done with all the objects of the category, but we can restrict ourselves to work with families of generators. Thus, universal properties can be characterized through iterated families of generators, which leads us in particular to an alternative version of the adjoint functor theorem. Similarly, the properties of relations or subobjects algebra can be investigated by this method. We end with a result that relates various forms of compactness through representable functors of generators.

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