Results 221 to 230 of about 1,083,294 (265)
Some of the next articles are maybe not open access.
2001
Database technology, as well as the bulk of data mining technology, is founded upon logic, with absolute notions of truth and falsehood, at least with respect to the data set. Patterns are discovered exhaustively, with carefully engineered algorithms devised to determine all patterns in a data set that belong to a certain class.
openaire +1 more source
Database technology, as well as the bulk of data mining technology, is founded upon logic, with absolute notions of truth and falsehood, at least with respect to the data set. Patterns are discovered exhaustively, with carefully engineered algorithms devised to determine all patterns in a data set that belong to a certain class.
openaire +1 more source
On Difference of Means with Incomplete Data
Biometrika, 1974SUMMARY An estimate of the difference of means is obtained when sampling from a bivariate normal distribution with variances o-2 and o- and correlation p, where some observations on either of the variables are missing. It is shown that this estimate has desirable properties.
Lin, Pi-Erh, Stivers, Lawrence E.
openaire +1 more source
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
openaire +2 more sources
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
openaire +1 more source
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 ...
openaire +1 more source
Probabilistic Skyline on Incomplete Data
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017The skyline query is important in database community. In recent years, the researches on incomplete data have been increasingly considered, especially for the skyline query. However, the existing skyline definition on incomplete data cannot provide users with valuable references.
Kaiqi Zhang 0001 +4 more
openaire +2 more sources
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
openaire +2 more sources
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.
openaire +1 more source
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
openaire +2 more sources
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
openaire +2 more sources
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.
openaire +2 more sources