Microarray Missing Values Imputation Methods: Critical Analysis Review
Mou'ath Hourani, Ibrahiem M. M. El Emary (2009)
Computer Science and Information Systems
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Mou'ath Hourani, Ibrahiem M. M. El Emary (2009)
Computer Science and Information Systems
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Ammar Shaker, Eyke Hüllermeier (2014)
International Journal of Applied Mathematics and Computer Science
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In this paper, we introduce a method for survival analysis on data streams. Survival analysis (also known as event history analysis) is an established statistical method for the study of temporal “events” or, more specifically, questions regarding the temporal distribution of the occurrence of events and their dependence on covariates of the data sources. To make this method applicable in the setting of data streams, we propose an adaptive variant of a model that is closely related to...
Fokoue, Ernest (2014)
Serdica Journal of Computing
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Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science, technology, medicine, public health, economics, business, linguistics and social science are bombarded by ever increasing flows of data begging to be analyzed efficiently and effectively. In this paper, we propose a rough idea of a possible taxonomy of big data, along with some of the most commonly used tools for handling each particular category of bigness. The dimensionality p of...
Piotr Kulczycki, Małgorzata Charytanowicz (2010)
International Journal of Applied Mathematics and Computer Science
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The aim of this paper is to provide a gradient clustering algorithm in its complete form, suitable for direct use without requiring a deeper statistical knowledge. The values of all parameters are effectively calculated using optimizing procedures. Moreover, an illustrative analysis of the meaning of particular parameters is shown, followed by the effects resulting from possible modifications with respect to their primarily assigned optimal values. The proposed algorithm does not demand...
Soren Feodor Nielsen (2003)
SORT
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In this paper we consider analysis of survival data with incomplete covariate information. We model the incomplete covariates as a random coarsening of the complete covariate, and an overview of the theory of coarsening at random is given. Various ways of estimating the parameters of the model for the survival data given the covariates are discussed and compared.
Daniel Defays (1997)
Qüestiió
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A natural strategy to protect the confidentiality of individual data is to aggregate them at the lowest possible level. Some studies realised in Eurostat on this topic will be presented: properties of classifications in clusters of fixed sizes, micro-aggregation as a generic method to protect the confidentiality of individual data, application to the Community Innovation Survey. The work performed in Eurostat will be put in line with other projects conducted at European level on the...
Tzavidis, Nikos, Lin, Yan-Xia (2006)
Journal of Applied Mathematics and Decision Sciences
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Victor L. Brailovsky, Michael Har-Even (1998)
Kybernetika
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Detecting a cluster structure is considered. This means solving either the problem of discovering a natural decomposition of data points into groups (clusters) or the problem of detecting clouds of data points of a specific form. In this paper both these problems are considered. To discover a cluster structure of a specific arrangement or a cloud of data of a specific form a class of nonlinear projections is introduced. Fitness functions that estimate to what extent a given subset of...
Yi Zhou, Bin Zhang (2024)
Applications of Mathematics
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This paper deals with the problem of estimating a covariance matrix from the data in two classes: (1) good data with the covariance matrix of interest and (2) contamination coming from a Gaussian distribution with a different covariance matrix. The ridge penalty is introduced to address the problem of high-dimensional challenges in estimating the covariance matrix from the two-class data model. A ridge estimator of the covariance matrix has a uniform expression and keeps positive-definite,...
Ammar Shaker, Eyke Hüllermeier (2014)
International Journal of Applied Mathematics and Computer Science
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Liu Bin, Zhang Hui, Liu Sifeng, Dang Yaoguo (2006)
Computer Science and Information Systems
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Piotr Kulczycki, Szymon Łukasik (2014)
International Journal of Applied Mathematics and Computer Science
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The paper deals with the issue of reducing the dimension and size of a data set (random sample) for exploratory data analysis procedures. The concept of the algorithm investigated here is based on linear transformation to a space of a smaller dimension, while retaining as much as possible the same distances between particular elements. Elements of the transformation matrix are computed using the metaheuristics of parallel fast simulated annealing. Moreover, elimination of or a decrease...
Milija Suknović, Milutin Čupić, Milan Martić, Darko Krulj (2005)
The Yugoslav Journal of Operations Research
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Y. W. Teh (2004)
Mathware and Soft Computing
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With the availability of very large data storage today, redundant data structures are no longer a big issue. However, an intelligent way of managing materialised projection and selection views that can lead to fast access of data is the central issue dealt with in this paper. A set of implementation steps for the data warehouse administrators or decision makers to improve the response time of queries is also defined. The study concludes that both attributes and tuples, are important...