Missing value imputation using a fuzzy clustering-based EM approach
Data preprocessing and cleansing play a vital role in data mining by ensuring good quality
of data. Data-cleansing tasks include imputation of missing values, identification of outliers …
of data. Data-cleansing tasks include imputation of missing values, identification of outliers …
Sparse principal component analysis with missing observations
K Lounici - High Dimensional Probability VI: The Banff Volume, 2013 - Springer
In this paper, we study the problem of sparse Principal Component Analysis (PCA) in the
high dimensional setting with missing observations. Our goal is to estimate the first principal …
high dimensional setting with missing observations. Our goal is to estimate the first principal …
Heuristically repopulated Bayesian ant colony optimization for treating missing values in large databases
The incomplete datasets with missing values are unsuitable for making strategic decisions
since they lead to biased results. This problem is even worse when the dataset is large and …
since they lead to biased results. This problem is even worse when the dataset is large and …
The ability of different imputation methods to preserve the significant genes and pathways in cancer
Deciphering important genes and pathways from incomplete gene expression data could
facilitate a better understanding of cancer. Different imputation methods can be applied to …
facilitate a better understanding of cancer. Different imputation methods can be applied to …
Estimation of missing values using optimised hybrid fuzzy c-means and majority vote for microarray data
Missing values are a huge constraint in microarray technologies towards improving and
identifying disease-causing genes. Estimating missing values is an undeniable scenario …
identifying disease-causing genes. Estimating missing values is an undeniable scenario …
Missing value imputation for RNA-sequencing data using statistical models: a comparative study
RNA-seq technology has been widely used as an alternative approach to traditional
microarrays in transcript analysis. Sometimes gene expression by sequencing, which …
microarrays in transcript analysis. Sometimes gene expression by sequencing, which …
Mining gene expression profile with missing values: An integration of kernel PCA and robust singular values decomposition
Background: Gene expression profiling and transcriptomics provide valuable information
about the role of genes that are differentially expressed between two or more samples. It is …
about the role of genes that are differentially expressed between two or more samples. It is …
[PDF][PDF] Modeling genotype and environment interaction for performance stability and adaptability of sugarcane cultivars
VO Otieno - 2016 - erepository.uonbi.ac.ke
1.1 Background Sugarcane farming in Kenya is mainly in South Nyanza, Nyando, Western
Kenya and South Coastal regions. It engages 8,000 people directly, over six million people …
Kenya and South Coastal regions. It engages 8,000 people directly, over six million people …
High-dimensional statistical and data mining techniques
G Zararsiz - Encyclopedia of Business Analytics and Optimization, 2014 - igi-global.com
Background HDD refers to data whose number of dimension is at least larger than the
dimensions considered in classical multivariate analysis in statistical theory. In many fields, it …
dimensions considered in classical multivariate analysis in statistical theory. In many fields, it …
Imputation of ignorable and non-ignorable missing values in large datasets using ACO with local search
Background: Presence of missing values in databases causes serious threats for knowledge
extraction. Especially in large databases which are integrated from multiple sources, the …
extraction. Especially in large databases which are integrated from multiple sources, the …