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On Fuzzy Clustering for Incomplete Spherical Data and for Incomplete Multivariate Categorical Data
2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS), 2018In this paper, six fuzzy clustering algorithms for incomplete data are proposed that use the optimal completion strategy, three of which are for incomplete spherical data and these of which are for incomplete categorical multivariate data. In numerical experiments using a real dataset, each of the proposed methods outperformed its counterpart method ...
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MISS: Analysis of Incomplete Data
Multivariate Behavioral Research, 1988MISS is a computer program written in the GAUSS programming language for the microcomputer (with DOS operating system and mathcoprocessor). It provides several options for incomplete data sets. First, it will produce maximum likelihood estimates of the covariance matrix and mean vector via the EM algorithm.
R, Schoenberg, G, Arminger
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Incomplete data management: a survey
Frontiers of Computer Science, 2017Incomplete data accompanies our life processes and covers almost all fields of scientific studies, as a result of delivery failure, no power of battery, accidental loss, etc. However, how to model, index, and query incomplete data incurs big challenges.
Xiaoye Miao +3 more
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ON CLASSIFICATION FOR INCOMPLETE MULTINORMAL DATA
Communications in Statistics - Simulation and Computation, 1973Using statistics proposed by Hocking and Smith (JASA (1968), 63, 159-173) a multinormal observation vector is classified into one of two normal populations whose mean vectors and covariance matrices are unknown, where the data used for estimation contains both full and partial records.
Smith, W. B., Zeis, C. D.
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A Selective Classifier for Incomplete Data
2008Classifiers based on feature selection (selective classifiers) are a kind of algorithms that can effectively improve the accuracy and efficiency of classification by deleting irrelevant or redundant attributes of a data set. Due to the complexity of processing incomplete data, however, most of them deal with complete data.
Jingnian Chen +3 more
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Incompleteness in Conceptual Data Modelling
2013Although conceptual data modelers can ”get creative” when designing entities and relationships to meet business requirements, they are highly constrained by the business rules which determine the details of how the entities and relationships combine. Typically, there is a delay in realising which business rules might be relevant and a further delay in ...
Peter Thanisch +5 more
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On decomposition for incomplete data
Fundam. Informaticae, 2003Summary: In this paper we present a method of data decomposition to avoid the necessity of reasoning on data with missing attribute values. This method can be applied to any algorithm of classifier induction. The original incomplete data is decomposed into data subsets without missing values.
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Concept Lattices of Incomplete Data
2012We present a method of constructing a concept lattice of a formal context with incomplete data. The lattice reduces to a classical concept lattice when the missing values are completed. The lattice also can reflect any known dependencies between the missing values.
Michal Krupka, Jan Lastovicka
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2015
Incomplete data are common in empirical research. The default solutions in software packages are very simplistic; the default is generally listwise deletion where a case with any variable missing is completely removed from the analysis. In multilevel data, missing values at the group level can be a serious problem.
Hox, J., van Buuren, S., Jolani, Shahab
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Incomplete data are common in empirical research. The default solutions in software packages are very simplistic; the default is generally listwise deletion where a case with any variable missing is completely removed from the analysis. In multilevel data, missing values at the group level can be a serious problem.
Hox, J., van Buuren, S., Jolani, Shahab
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Generating Incomplete Data with DataZapper
2010A nearly universal problem with real data is that they are incomplete, with some values missing. Furthermore, the ways in which values can go missing are quite varied, with arbitrary interdependencies between variables and their values leading to missing values.
Yingying Wen +2 more
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