Relevant bibliographies by topics / Numeric and categorical data

Contents

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

Academic literature on the topic 'Numeric and categorical data'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Numeric and categorical data"

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Suguna, J., and M. Arul Selvi. "Ensemble Fuzzy Clustering for Mixed Numeric and Categorical Data." International Journal of Computer Applications 42, no. 3 (March 31, 2012): 19–23. http://dx.doi.org/10.5120/5672-7705.

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Ji, Jinchao, Wei Pang, Zairong Li, Fei He, Guozhong Feng, and Xiaowei Zhao. "Clustering Mixed Numeric and Categorical Data With Cuckoo Search." IEEE Access 8 (2020): 30988–1003. http://dx.doi.org/10.1109/access.2020.2973216.

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Wu, Chengyuan, and Carol Anne Hargreaves. "Topological Machine Learning for Mixed Numeric and Categorical Data." International Journal on Artificial Intelligence Tools 30, no. 05 (August 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 proposed method, we use theory from topological data analysis such as persistent homology, persistence diagrams and Wasserstein distance to study mixed data. The performance of the proposed method is demonstrated by experiments on a real-world heart disease dataset. Experimental results show that our topological method outperforms several state-of-the-art algorithms in the prediction of heart disease.
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Lee, Kyung Mi, and Keon Myung Lee. "A Locality Sensitive Hashing Technique for Categorical Data." Applied Mechanics and Materials 241-244 (December 2012): 3159–64. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.3159.

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The measured data may contain various types of attributes such as continuous, categorical, and set-valued attributes. Several locality-sensitive hashing techniques, which enable to find similar pairs of data in a fast and approximate way, have been developed for data with either numeric or set-valued attributes. This paper introduces a new locality sensitive-hashing technique applicable to data with categorical attributes.
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Arunprabha, K., and V. Bhuvaneswari. "Comparing K-Value Estimation for Categorical and Numeric Data Clustring." International Journal of Computer Applications 11, no. 3 (December 10, 2010): 4–7. http://dx.doi.org/10.5120/1565-1875.

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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 (November 30, 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 designed for numerical and categorical datasets that are employed to produce corresponding clusters. On the other side, latent class clustering is a model-based clustering used for any type of data. The numbers of clusters base on the estimation of the probability model used. The best clustering method recommends LCC, which provides higher accuracy and the smallest standard deviation ratio. However, both LCC and cluster ensemble methods produce evaluation values that are not much different as the application method used potential village data in Bengkulu Province for clustering.
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Battaglia, Elena, Simone Celano, and Ruggero G. Pensa. "Differentially Private Distance Learning in Categorical Data." Data Mining and Knowledge Discovery 35, no. 5 (July 13, 2021): 2050–88. http://dx.doi.org/10.1007/s10618-021-00778-0.

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AbstractMost privacy-preserving machine learning methods are designed around continuous or numeric data, but categorical attributes are common in many application scenarios, including clinical and health records, census and survey data. Distance-based methods, in particular, have limited applicability to categorical data, since they do not capture the complexity of the relationships among different values of a categorical attribute. Although distance learning algorithms exist for categorical data, they may disclose private information about individual records if applied to a secret dataset. To address this problem, we introduce a differentially private family of algorithms for learning distances between any pair of values of a categorical attribute according to the way they are co-distributed with the values of other categorical attributes forming the so-called context. We define different variants of our algorithm and we show empirically that our approach consumes little privacy budget while providing accurate distances, making it suitable in distance-based applications, such as clustering and classification.
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Ji, Jinchao, Yongbing Chen, Guozhong Feng, Xiaowei Zhao, and Fei He. "Clustering mixed numeric and categorical data with artificial bee colony strategy." Journal of Intelligent & Fuzzy Systems 36, no. 2 (March 16, 2019): 1521–30. http://dx.doi.org/10.3233/jifs-18146.

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Ahmad, Amir, and Lipika Dey. "A k-mean clustering algorithm for mixed numeric and categorical data." Data & Knowledge Engineering 63, no. 2 (November 2007): 503–27. http://dx.doi.org/10.1016/j.datak.2007.03.016.

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Ji, Jinchao, Ruonan Li, Wei Pang, Fei He, Guozhong Feng, and Xiaowei Zhao. "A Multi-View Clustering Algorithm for Mixed Numeric and Categorical Data." IEEE Access 9 (2021): 24913–24. http://dx.doi.org/10.1109/access.2021.3057113.

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Dissertations / Theses on the topic "Numeric and categorical data"

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Jia, Hong. "Clustering of categorical and numerical data without knowing cluster number." HKBU Institutional Repository, 2013. http://repository.hkbu.edu.hk/etd_ra/1495.

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Suarez, Alvarez Maria Del Mar. "Design and analysis of clustering algorithms for numerical, categorical and mixed data." Thesis, Cardiff University, 2010. http://orca.cf.ac.uk/54131/.

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In recent times, several machine learning techniques have been applied successfully to discover useful knowledge from data. Cluster analysis that aims at finding similar subgroups from a large heterogeneous collection of records, is one o f the most useful and popular of the available techniques o f data mining. The purpose of this research is to design and analyse clustering algorithms for numerical, categorical and mixed data sets. Most clustering algorithms are limited to either numerical or categorical attributes. Datasets with mixed types o f attributes are common in real life and so to design and analyse clustering algorithms for mixed data sets is quite timely. Determining the optimal solution to the clustering problem is NP-hard. Therefore, it is necessary to find solutions that are regarded as “good enough” quickly. Similarity is a fundamental concept for the definition of a cluster. It is very common to calculate the similarity or dissimilarity between two features using a distance measure. Attributes with large ranges will implicitly assign larger contributions to the metrics than the application to attributes with small ranges. There are only a few papers especially devoted to normalisation methods. Usually data is scaled to unit range. This does not secure equal average contributions of all features to the similarity measure. For that reason, a main part o f this thesis is devoted to normalisation.
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Hjerpe, Adam. "Computing Random Forests Variable Importance Measures (VIM) on Mixed Numerical and Categorical Data." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185496.

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The Random Forest model is commonly used as a predictor function and the model have been proven useful in a variety of applications. Their popularity stems from the combination of providing high prediction accuracy, their ability to model high dimensional complex data, and their applicability under predictor correlations. This report investigates the random forest variable importance measure (VIM) as a means to find a ranking of important variables. The robustness of the VIM under imputation of categorical noise, and the capability to differentiate informative predictors from non-informative variables is investigated. The selection of variables may improve robustness of the predictor, improve the prediction accuracy, reduce computational time, and may serve as a exploratory data analysis tool. In addition the partial dependency plot obtained from the random forest model is examined as a means to find underlying relations in a non-linear simulation study.
Random Forest (RF) är en populär prediktormodell som visat goda resultat vid en stor uppsättning applikationsstudier. Modellen ger hög prediktionsprecision, har förmåga att modellera komplex högdimensionell data och modellen har vidare visat goda resultat vid interkorrelerade prediktorvariabler. Detta projekt undersöker ett mått, variabel importance measure (VIM) erhållna från RF modellen, för att beräkna graden av association mellan prediktorvariabler och målvariabeln. Projektet undersöker känsligheten hos VIM vid kvalitativt prediktorbrus och undersöker VIMs förmåga att differentiera prediktiva variabler från variabler som endast, med aveende på målvariableln, beskriver brus. Att differentiera prediktiva variabler vid övervakad inlärning kan användas till att öka robustheten hos klassificerare, öka prediktionsprecisionen, reducera data dimensionalitet och VIM kan användas som ett verktyg för att utforska relationer mellan prediktorvariabler och målvariablel.
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Kirsch, Matthew Robert. "Signal Processing Algorithms for Analysis of Categorical and Numerical Time Series: Application to Sleep Study Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1278606480.

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Obry, Tom. "Apprentissage numérique et symbolique pour le diagnostic et la réparation automobile." Thesis, Toulouse, INSA, 2020. http://www.theses.fr/2020ISAT0014.

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Le clustering est une des méthodes issues de l'apprentissage non-supervisé qui vise à partitionner un ensemble de données en différents groupes homogènes au sens d’un critère de similarité. Les données de chaque groupe partagent alors des caractéristiques communes. DyClee est un classifieur qui réalise une classification à partir de données numériques arrivant en flux continu et qui propose un mécanisme d’adaptation pour mettre à jour cette classification réalisant ainsi un clustering dynamique en accord avec les évolutions du système ou procédé suivi. Néanmoins la seule prise en compte des attributs numériques ne permet pas d’appréhender tous les champs d’application. Dans cet objectif de généralisation, cette thèse propose d’une part une extension aux données catégorielles nominales, d’autre part une extension aux données mixtes. Des approches de clustering hiérarchique sont également proposées afin d’assister les experts dans l’interprétation des clusters obtenus et dans la validation des partitions générées. L'algorithme présenté, appelé DyClee Mixte, peut être appliqué dans des divers domaines applicatifs. Dans le cas de cette thèse, il est utilisé dans le domaine du diagnostic automobile
Clustering is one of the methods resulting from unsupervised learning which aims to partition a data set into different homogeneous groups in the sense of a similarity criterion. The data in each group then share common characteristics. DyClee is a classifier that performs a classification based on digital data arriving in a continuous flow and which proposes an adaptation mechanism to update this classification, thus performing dynamic clustering in accordance with the evolution of the system or process being followed. Nevertheless, the only consideration of numerical attributes does not allow to apprehend all the fields of application. In this generalization objective, this thesis proposes on the one hand an extension to nominal categorical data, and on the other hand an extension to mixed data. Hierarchical clustering approaches are also proposed in order to assist the experts in the interpretation of the obtained clusters and in the validation of the generated partitions. The presented algorithm, called Mixed DyClee, can be applied in various application domains. In the case of this thesis, it is used in the field of automotive diagnostics
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Bashon, Yasmina M. "Contributions to fuzzy object comparison and applications. Similarity measures for fuzzy and heterogeneous data and their applications." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6305.

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This thesis makes an original contribution to knowledge in the fi eld of data objects' comparison where the objects are described by attributes of fuzzy or heterogeneous (numeric and symbolic) data types. Many real world database systems and applications require information management components that provide support for managing such imperfect and heterogeneous data objects. For example, with new online information made available from various sources, in semi-structured, structured or unstructured representations, new information usage and search algorithms must consider where such data collections may contain objects/records with di fferent types of data: fuzzy, numerical and categorical for the same attributes. New approaches of similarity have been presented in this research to support such data comparison. A generalisation of both geometric and set theoretical similarity models has enabled propose new similarity measures presented in this thesis, to handle the vagueness (fuzzy data type) within data objects. A framework of new and unif ied similarity measures for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes has also been introduced. Examples are used to illustrate, compare and discuss the applications and e fficiency of the proposed approaches to heterogeneous data comparison.
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Bashon, Yasmina Massoud. "Contributions to fuzzy object comparison and applications : similarity measures for fuzzy and heterogeneous data and their applications." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6305.

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This thesis makes an original contribution to knowledge in the fi eld of data objects' comparison where the objects are described by attributes of fuzzy or heterogeneous (numeric and symbolic) data types. Many real world database systems and applications require information management components that provide support for managing such imperfect and heterogeneous data objects. For example, with new online information made available from various sources, in semi-structured, structured or unstructured representations, new information usage and search algorithms must consider where such data collections may contain objects/records with di fferent types of data: fuzzy, numerical and categorical for the same attributes. New approaches of similarity have been presented in this research to support such data comparison. A generalisation of both geometric and set theoretical similarity models has enabled propose new similarity measures presented in this thesis, to handle the vagueness (fuzzy data type) within data objects. A framework of new and unif ied similarity measures for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes has also been introduced. Examples are used to illustrate, compare and discuss the applications and e fficiency of the proposed approaches to heterogeneous data comparison.
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Hollingsworth, Jason Michael. "Foundational Data Repository for Numeric Engine Validation." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2661.pdf.

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Läuter, Henning, and Ayad Ramadan. "Statistical Scaling of Categorical Data." Universität Potsdam, 2010. http://opus.kobv.de/ubp/volltexte/2011/4956/.

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Estimation and testing of distributions in metric spaces are well known. R.A. Fisher, J. Neyman, W. Cochran and M. Bartlett achieved essential results on the statistical analysis of categorical data. In the last 40 years many other statisticians found important results in this field. Often data sets contain categorical data, e.g. levels of factors or names. There does not exist any ordering or any distance between these categories. At each level there are measured some metric or categorical values. We introduce a new method of scaling based on statistical decisions. For this we define empirical probabilities for the original observations and find a class of distributions in a metric space where these empirical probabilities can be found as approximations for equivalently defined probabilities. With this method we identify probabilities connected with the categorical data and probabilities in metric spaces. Here we get a mapping from the levels of factors or names into points of a metric space. This mapping yields the scale for the categorical data. From the statistical point of view we use multivariate statistical methods, we calculate maximum likelihood estimations and compare different approaches for scaling.
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Zhang, Yiqun. "Advances in categorical data clustering." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/658.

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Categorical data are common in various research areas, and clustering is a prevalent technique used for analyse them. However, two challenging problems are encountered in categorical data clustering analysis. The first is that most categorical data distance metrics were actually proposed for nominal data (i.e., a categorical data set that comprises only nominal attributes), ignoring the fact that ordinal attributes are also common in various categorical data sets. As a result, these nominal data distance metrics cannot account for the order information of ordinal attributes and may thus inappropriately measure the distances for ordinal data (i.e., a categorical data set that comprises only ordinal attributes) and mixed categorical data (i.e., a categorical data set that comprises both ordinal and nominal attributes). The second problem is that most hierarchical clustering approaches were actually designed for numerical data and have very high computation costs; that is, with time complexity O(N2) for a data set with N data objects. These issues have presented huge obstacles to the clustering analysis of categorical data. To address the ordinal data distance measurement problem, we studied the characteristics of ordered possible values (also called 'categories' interchangeably in this thesis) of ordinal attributes and propose a novel ordinal data distance metric, which we call the Entropy-Based Distance Metric (EBDM), to quantify the distances between ordinal categories. The EBDM adopts cumulative entropy as a measure to indicate the amount of information in the ordinal categories and simulates the thinking process of changing one's mind between two ordered choices to quantify the distances according to the amount of information in the ordinal categories. The order relationship and the statistical information of the ordinal categories are both considered by the EBDM for more appropriate distance measurement. Experimental results illustrate the superiority of the proposed EBDM in ordinal data clustering. In addition to designing an ordinal data distance metric, we further propose a unified categorical data distance metric that is suitable for distance measurement of all three types of categorical data (i.e., ordinal data, nominal data, and mixed categorical data). The extended version uniformly defines distances and attribute weights for both ordinal and nominal attributes, by which the distances measured for the two types of attributes of a mixed categorical data can be directly combined to obtain the overall distances between data objects with no information loss. Extensive experiments on all three types of categorical data sets demonstrate the effectiveness of the unified distance metric in clustering analysis of categorical data. To address the hierarchical clustering problem of large-scale categorical data, we propose a fast hierarchical clustering framework called the Growing Multi-layer Topology Training (GMTT). The most significant merit of this framework is its ability to reduce the time complexity of most existing hierarchical clustering frameworks (i.e., O(N2)) to O(N1.5) without sacrificing the quality (i.e., clustering accuracy and hierarchical details) of the constructed hierarchy. According to our design, the GMTT framework is applicable to categorical data clustering simply by adopting a categorical data distance metric. To make the GMTT framework suitable for the processing of streaming categorical data, we also provide an incremental version of GMTT that can dynamically adopt new inputs into the hierarchy via local updating. Theoretical analysis proves that the GMTT frameworks have time complexity O(N1.5). Extensive experiments show the efficacy of the GMTT frameworks and demonstrate that they achieve more competitive categorical data clustering performance by adopting the proposed unified distance metric.
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Books on the topic "Numeric and categorical data"

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Categorical data analysis. 2nd ed. New York: Wiley-Interscience, 2002.

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

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Categorical data analysis. New York: Wiley, 1990.

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Institute, SAS, ed. Visualizing categorical data. Cary, NC: SAS Institute, 2001.

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Simonoff, Jeffrey S. Analyzing Categorical Data. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21727-7.

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Categorical data analysis. 3rd ed. Hoboken, NJ: Wiley, 2012.

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Yang, Keming. Categorical Data Analysis. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2014. http://dx.doi.org/10.4135/9781473915466.

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Le, Chap T. Applied categorical data analysis. New York: Wiley, 1998.

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Applied categorical data analysis. New York: M. Dekker, 1987.

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Sutradhar, Brajendra C. Longitudinal Categorical Data Analysis. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-2137-9.

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Book chapters on the topic "Numeric and categorical data"

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Kuo, Huang-Cheng. "A Divisive Ordering Algorithm for Mapping Categorical Data to Numeric Data." In Lecture Notes in Computer Science, 979–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11552451_135.

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Ahmad, Amir, and Lipika Dey. "Algorithm for Fuzzy Clustering of Mixed Data with Numeric and Categorical Attributes." In Distributed Computing and Internet Technology, 561–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11604655_63.

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Martarelli, Nádia Junqueira, and Marcelo Seido Nagano. "Optimization of the Numeric and Categorical Attribute Weights in KAMILA Mixed Data Clustering Algorithm." In Intelligent Data Engineering and Automated Learning – IDEAL 2019, 20–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33607-3_3.

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Chen, Guanhua, Xiuli Ma, Dongqing Yang, Shiwei Tang, and Meng Shuai. "A Bipartite Graph Framework for Summarizing High-Dimensional Binary, Categorical and Numeric Data." In Lecture Notes in Computer Science, 580–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02279-1_41.

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Silva, Joaquim, Gabriel Lopes, and António Falcão. "Mining Causality from Non-categorical Numerical Data." In Behavior Computing, 215–27. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2969-1_13.

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Feng, Xiaodong, Sen Wu, and Yanchi Liu. "Imputing Missing Values for Mixed Numeric and Categorical Attributes Based on Incomplete Data Hierarchical Clustering." In Knowledge Science, Engineering and Management, 414–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25975-3_37.

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Caruso, Giulia, Adelia Evangelista, and Stefano Antonio Gattone. "Profiling visitors of a national park in Italy through unsupervised classification of mixed data." In Proceedings e report, 135–40. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.27.

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Cluster analysis has for long been an effective tool for analysing data. Thus, several disciplines, such as marketing, psychology and computer sciences, just to mention a few, did take advantage from its contribution over time. Traditionally, this kind of algorithm concentrates only on numerical or categorical data at a time. In this work, instead, we analyse a dataset composed of mixed data, namely both numerical than categorical ones. More precisely, we focus on profiling visitors of the National Park of Majella in the Abruzzo region of Italy, which observations are characterized by variables such as gender, age, profession, expectations and satisfaction rate on park services. Applying a standard clustering procedure would be wholly inappropriate in this case. Therefore, we hereby propose an unsupervised classification of mixed data, a specific procedure capable of processing both numerical than categorical variables simultaneously, releasing truly precious information. In conclusion, our application therefore emphasizes how cluster analysis for mixed data can lead to discover particularly informative patterns, allowing to lay the groundwork for an accurate customers profiling, starting point for a detailed marketing analysis.
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Rastogi, Rohit, Saumya Agarwal, Palak Sharma, Uarvarshi Kaul, and Shilpi Jain. "Unsupervised Classification of Mixed Data Type of Attributes Using Genetic Algorithm (Numeric, Categorical, Ordinal, Binary, Ratio-Scaled)." In Advances in Intelligent Systems and Computing, 121–31. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1771-8_11.

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Cheung, Yiu-ming, and Hong Jia. "A Unified Metric for Categorical and Numerical Attributes in Data Clustering." In Advances in Knowledge Discovery and Data Mining, 135–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37456-2_12.

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Alghanmi, Nouf, and Xiao-Jun Zeng. "A Hybrid Regression Model for Mixed Numerical and Categorical Data." In Advances in Intelligent Systems and Computing, 369–76. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29933-0_31.

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Conference papers on the topic "Numeric and categorical data"

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Reddy, M. V. Jagannatha, and B. Kavitha. "Efficient ensemble algorithm for mixed numeric and categorical data." In 2010 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2010. http://dx.doi.org/10.1109/iccic.2010.5705738.

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Han, Xiao, Yahui Yang, Qingni Shen, and Min Xia. "An Improved ART 2-A Model for Mixed Numeric and Categorical Data." In 2009 International Conference on Information Engineering and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/iciecs.2009.5365746.

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Wang, Shuyun, Yingjie Fan, Chenghong Zhang, HeXiang Xu, Xiulan Hao, and Yunfa Hu. "Entropy Based Clustering of Data Streams with Mixed Numeric and Categorical Values." In Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008). IEEE, 2008. http://dx.doi.org/10.1109/icis.2008.57.

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Suematsu, Haruka, Sayaka Yagi, Takayuki Itoh, Yosuke Motohashi, Kenji Aoki, and Satoshi Morinaga. "A Heatmap-Based Time-Varying Multi-variate Data Visualization Unifying Numeric and Categorical Variables." In 2014 18th International Conference on Information Visualisation (IV). IEEE, 2014. http://dx.doi.org/10.1109/iv.2014.25.

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Li, Taoying, and Yan Chen. "A Weight Entropy k-Means Algorithm for Clustering Dataset with Mixed Numeric and Categorical Data." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.32.

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Li, Jie, Xinbo Gao, and Licheng Jiao. "A GA-based clustering algorithm for large data sets with mixed numeric and categorical values." In Third International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Hanqing Lu and Tianxu Zhang. SPIE, 2003. http://dx.doi.org/10.1117/12.538864.

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Andreopoulos, Bill, Aijun An, and Xiaogang Wang. "Clustering mixed numerical and low quality categorical data." In the 2nd international workshop. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1077501.1077517.

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Liang, Wen-Bin, Chang-Dong Wang, and Jian-Huang Lai. "Weighted numerical and categorical attribute clustering in data streams." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966237.

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Bacaksiz, Ahmet Hifzi, and Eren Esgin. "Extraction of Numerical data from Categorical Data Set and Artificial Neural Networks." In 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE, 2019. http://dx.doi.org/10.1109/ismsit.2019.8932767.

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Zhang, Kai, Qiaojun Wang, Zhengzhang Chen, Ivan Marsic, Vipin Kumar, Guofei Jiang, and Jie Zhang. "From Categorical to Numerical: Multiple Transitive Distance Learning and Embedding." In Proceedings of the 2015 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974010.6.

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Reports on the topic "Numeric and categorical data"

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Leupp, D. G., S. Kelly, and D. E. Bridges. A Comparison of Numeric Data Entry with Touch-Sensitive and Conventional Numeric Keypads. Fort Belvoir, VA: Defense Technical Information Center, February 1985. http://dx.doi.org/10.21236/ada153276.

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Tueller, Stephen, Richard Van Dorn, and Georgiy Bobashev. Visualization of Categorical Longitudinal and Times Series Data. RTI Press, February 2016. http://dx.doi.org/10.3768/rtipress.2016.mr.0033.1602.

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Pruett, Richard K. WDMET Numeric and Descriptive Data User Interface Development Project. Fort Belvoir, VA: Defense Technical Information Center, July 1996. http://dx.doi.org/10.21236/ada286979.

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Rugg, David J. TableSim--A program for analysis of small-sample categorical data. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Research Station, 2003. http://dx.doi.org/10.2737/nc-gtr-232.

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Boden, T. A., F. M. Jr O`Hara, and F. W. Stoss. CDIAC catalog of numeric data packages and computer model packages. Office of Scientific and Technical Information (OSTI), May 1993. http://dx.doi.org/10.2172/10176843.

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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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Abstract:
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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Peterson, James T. CATDAT : A Program for Parametric and Nonparametric Categorical Data Analysis : User's Manual Version 1.0, 1998-1999 Progress Report. Office of Scientific and Technical Information (OSTI), December 1999. http://dx.doi.org/10.2172/756625.

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Lumpkin, Shamsie, Isaac Parrish, Austin Terrell, and Dwayne Accardo. Pain Control: Opioid vs. Nonopioid Analgesia During the Immediate Postoperative Period. University of Tennessee Health Science Center, July 2021. http://dx.doi.org/10.21007/con.dnp.2021.0008.

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Background Opioid analgesia has become the mainstay for acute pain management in the postoperative setting. However, the use of opioid medications comes with significant risks and side effects. Due to increasing numbers of prescriptions to those with chronic pain, opioid medications have become more expensive while becoming less effective due to the buildup of patient tolerance. The idea of opioid-free analgesic techniques has rarely been breached in many hospitals. Emerging research has shown that opioid-sparing approaches have resulted in lower reported pain scores across the board, as well as significant cost reductions to hospitals and insurance agencies. In addition to providing adequate pain relief, the predicted cost burden of an opioid-free or opioid-sparing approach is significantly less than traditional methods. Methods The following groups were considered in our inclusion criteria: those who speak the English language, all races and ethnicities, male or female, home medications, those who are at least 18 years of age and able to provide written informed consent, those undergoing inpatient or same-day surgical procedures. In addition, our scoping review includes the following exclusion criteria: those who are non-English speaking, those who are less than 18 years of age, those who are not undergoing surgical procedures while admitted, those who are unable to provide numeric pain score due to clinical status, those who are unable to provide written informed consent, and those who decline participation in the study. Data was extracted by one reviewer and verified by the remaining two group members. Extraction was divided as equally as possible among the 11 listed references. Discrepancies in data extraction were discussed between the article reviewer, project editor, and group leader. Results We identified nine primary sources addressing the use of ketamine as an alternative to opioid analgesia and post-operative pain control. Our findings indicate a positive correlation between perioperative ketamine administration and postoperative pain control. While this information provides insight on opioid-free analgesia, it also revealed the limited amount of research conducted in this area of practice. The strategies for several of the clinical trials limited ketamine administration to a small niche of patients. The included studies provided evidence for lower pain scores, reductions in opioid consumption, and better patient outcomes. Implications for Nursing Practice Based on the results of the studies’ randomized controlled trials and meta-analyses, the effects of ketamine are shown as an adequate analgesic alternative to opioids postoperatively. The cited resources showed that ketamine can be used as a sole agent, or combined effectively with reduced doses of opioids for multimodal therapy. There were noted limitations in some of the research articles. Not all of the cited studies were able to include definitive evidence of proper blinding techniques or randomization methods. Small sample sizes and the inclusion of specific patient populations identified within several of the studies can skew data in one direction or another; therefore, significant clinical results cannot be generalized to patient populations across the board.
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