Results 21 to 30 of about 1,120,836 (288)
HABOS clustering algorithm for categorical data
工程科学学报, 2016 The clustering algorithm based on sparse feature vector for categorical attributes(CABOSFVC) is an efficient high-dimensional clustering method for categorical data.
WU Sen, JIANG Dan-dan, WANG Qiang
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Categorical Data-Specifications
Theory and Applications of Categories, 1995 Summary: We introduce MD-sketches, which are a particular kind of Finite Sum sketches. Two interesting results about MD-sketches are proved. First, we show that, given two MD-sketches, it is algorithmically decidable whether their model categories are equivalent. Next we show that data-specifications as used in database-design and software engineering,
Piessens, Frank, Steegmans, Eric
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Survey on categorical data for neural networks
Journal of Big Data, 2020 This survey investigates current techniques for representing qualitative data for use as input to neural networks. Techniques for using qualitative data in neural networks are well known.
John T. Hancock, Taghi M. Khoshgoftaar
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Slovenian Veterinary Research, 2023 Bluetongue (BT) is a non-contagious virus in the Reoviridae family that infects both wild and domestic animals. It causes economic losses and reduces infected animals' production and reproduction.
Iman E. El-Araby +3 more
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SIMPLE ALGORITHM TO CONSTRUCT CIRCULAR CONFIDENCE REGIONS IN CORRESPONDENCE ANALYSIS USING R
Barekeng, 2022 Correspondence analysis has been widely applied in various fields as a graphical method to depict the association structure between two categorical random variables on a low-dimensional plot. This study built a simple algorithm to determine the principal
Karunia Eka Lestari +2 more
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Smoothing Categorical Data [PDF]
, 2012 Global models of a dataset reflect not only the large scale structure of the data distribution, they also reflect small(er) scale structure. Hence, if one wants to see the large scale structure, one should somehow subtract this smaller scale structure from the model. While for some kinds of model --- such as boosted classifiers --- it is easy to see
Siebes, Arno, Kersten, René
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Missing Categorical Data Imputation and Individual Observation Level Imputation
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2014 Traditional missing data techniques of imputation schemes focus on prediction of the missing value based on other observed values. In the case of continuous missing data the imputation of missing values often focuses on regression models.
Pavel Zimmermann +2 more
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Role of Categorical Variables in Multicollinearity in the Linear Regression Model [PDF]
, 2007 The present article discusses the role of categorical variable in the problem of multicollinearity in linear regression model. It exposes the diagnostic tool condition number to linear regression models with categorical explanatory variables and analyzes
Toutenburg, Helge +2 more
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Change detection in categorical evolving data streams
, 2014 Detecting change in evolving data streams is a central issue for accurate adaptive learning. In real world applications, data streams have categorical features, and changes induced in the data distribution of these categorical features have not been ...
Ienco, Dino +7 more
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Tree-Based Contrast Subspace Mining for Categorical Data
International Journal of Computational Intelligence Systems, 2020 Mining contrast subspace has emerged to find subspaces where a particular queried object is most similar to the target class against the non-target class in a two-class data set.
Florence Sia, Rayner Alfred, Yuto Lim
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