Search results
Results from the WOW.Com Content Network
Data reduction is the transformation of numerical or alphabetical digital information derived empirically or experimentally into a corrected, ordered, and simplified form. . The purpose of data reduction can be two-fold: reduce the number of data records by eliminating invalid data or produce summary data and statistics at different aggregation levels for various applications
Dimensionality reduction; ... Data mining is the process of extracting and ... and data snooping refer to the use of data mining methods to sample parts of a larger ...
Methods are commonly divided into linear and nonlinear approaches. [1] Linear approaches can be further divided into feature selection and feature extraction. [2] Dimensionality reduction can be used for noise reduction, data visualization, cluster analysis, or as an intermediate step to facilitate other analyses.
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.
Instance selection (or dataset reduction, or dataset condensation) is an important data pre-processing step that can be applied in many machine learning (or data mining) tasks. [1] Approaches for instance selection can be applied for reducing the original dataset to a manageable volume, leading to a reduction of the computational resources that ...
A nice feature of constructive induction methods such as MDR is the ability to use any data mining or machine learning method to analyze the new representation of the data. Decision trees , neural networks , or a naive Bayes classifier could be used in combination with measures of model quality such as balanced accuracy [ 11 ] [ 12 ] and mutual ...
Many methods have been invented to extract a low-dimensional structure from the data set, such as principal component analysis and multidimensional scaling. [27] However, it is important to note that the problem itself is ill-posed, since many different topological features can be found in the same data set.
In information theory, data compression, source coding, [1] or bit-rate reduction is the process of encoding information using fewer bits than the original representation. [2] Any particular compression is either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in ...