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Manipulation of data before it is analyzed
Data preprocessing can refer to manipulation, filtration or augmentation of data before it is analyzed, and is often an important step in the data mining
Data_preprocessing
Topics referred to by the same term
Preprocessing may refer to the following topics in computer science: Preprocessor, a program that processes its input data to produce output that is used
Preprocessing
Field of study to extract knowledge from data
data preprocessing, and supervised learning. Cloud computing can offer access to large amounts of computational power and storage. In big data, where
Data_science
Technique in neural networks for learning joint representations of text and images
dataset, so this preprocessing step roughly whitens the image tensor. These numbers slightly differ from the standard preprocessing for ImageNet, which
Contrastive Language–Image Pre-training
Contrastive_Language–Image_Pre-training
Process of digitizing data
Accounting Essays and Assignments. ISBN 978-1312069312. "Data Preprocessing Techniques for Data Mining" (PDF). "Information Technology". "How hardware and
Data_entry
Grouping a set of objects by similarity
that involves trial and failure. It is often necessary to modify data preprocessing and model parameters until the result achieves the desired properties
Cluster_analysis
Unit of information
data Data (computer science) Data acquisition Data analysis Data bank Data cable Data center Data curation Data domain Data element Data farming Data
Data
Method of data analysis
technique with applications in exploratory data analysis, visualization and data preprocessing. The data are linearly transformed onto a new coordinate
Principal_component_analysis
Method used to normalize the range of independent variables
or features of data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step. Since the
Feature_scaling
Suite of machine learning software written in Java
modeling algorithms implemented in other programming languages, plus data preprocessing utilities in C, and a makefile-based system for running machine learning
Weka_(software)
Simplifying data to facilitate analysis
conditionality and equivariance. Data cleansing Data editing Data preprocessing Data wrangling "Travel Time Data Collection Handbook" (PDF). Retrieved
Data_reduction
Task of finding records in a data set that refer to same entity across different sources
linkage (also known as data matching, data linkage, entity resolution, and many other terms) is the task of finding records in a data set that refer to the
Record_linkage
Measurement of algorithmic bias
be applied to machine learning algorithms in three different ways: data preprocessing, optimization during software training, or post-processing results
Fairness_(machine_learning)
Approach to artificial intelligence emphasizing data quality and management
learning Data preprocessing Training data Data quality Feature engineering MLOps Data governance Ng, Andrew (2021). "MLOps: From Model-centric to Data-centric
Data-centric_AI
Process of merging big data
datasets?" Data preparation Data fusion Data wrangling Data cleansing Data editing Data scraping Data curation Data preprocessing Alteryx Analytics Brings
Data_blending
Measure of the joint variability
feature dimensionality in data preprocessing. The principal components are the dimensions that explain the most variance in the data. A well known application
Covariance
Study of collection and analysis of data
collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it
Statistics
Family of convolutional neural networks
The stem (data ingestion): The first few convolutional layers perform data preprocessing to downscale images to a smaller size. The body (data processing):
Inception (deep learning architecture)
Inception_(deep_learning_architecture)
Gathering information for analysis
Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables
Data_collection
Data compression approach allowing perfect reconstruction of the original data
often used as a component within lossy data compression technologies (e.g. lossless mid/side joint stereo preprocessing by MP3 encoders and other lossy audio
Lossless_compression
String searching algorithm
1 {\displaystyle n-m+1} ), Boyer–Moore uses information gained by preprocessing P to skip as many alignments as possible. Previous to the introduction
Boyer–Moore string-search algorithm
Boyer–Moore_string-search_algorithm
keep data quality and governance high. Machine learning Feature engineering Data pipeline Data warehouse Data lake MLOps Data preprocessing Big data "Feature
Feature_store
Measurable property or characteristic
Zhou, Rongtian; Chen, Rongda; Lai, Kin Keung (2022-01-26). "Missing Data Preprocessing in Credit Classification: One-Hot Encoding or Imputation?". Emerging
Feature_(machine_learning)
Hydrologic simulation software suite
software is supported by an interactive graphics-based interface for data-preprocessing, discretization of the soil profile, and graphic presentation of the
Hydrus_(software)
Algorithmically generated data that have a similar distribution as sampled data
Synthetic data are artificially generated data not produced by real-world events. Typically created using algorithms, synthetic data can be deployed to
Synthetic_data
Subfield of control engineering
the features to overcome the curse of dimensionality, so often some data preprocessing techniques like Principal component analysis(PCA), Linear discriminant
Fault_detection_and_isolation
two groups: open data and non-open data. The datasets from various governmental-bodies are presented in List of open government data sites. The datasets
List of datasets for machine-learning research
List_of_datasets_for_machine-learning_research
Two different methods for presenting tabular data
different presentations for tabular data. The terms used vary by community and software: Wide and long: Common in modern data science and time-series analysis
Wide_and_narrow_data
Processing mode
developed for biomedical applications. The CaseOLAP platform includes data preprocessing (e.g., downloading, extraction, and parsing text documents), indexing
Online_analytical_processing
Open source version system
represent the process of building ML datasets and models, from how data is preprocessed to how models are trained and evaluated. Pipelines can also be used
Data Version Control (software)
Data_Version_Control_(software)
Observed inability to reproduce scientific studies
details—such as dataset preprocessing, exact model hyperparameters, random seeds, and hardware configurations—and failure to release code or data used in experiments
Replication_crisis
Process in machine learning and statistics
scales (units) and insensitive to outliers, and thus, require little data preprocessing such as normalization. Regularized random forest (RRF) is one type
Feature_selection
Program that processes input for another program
its input data to produce output that is used as input in another program. The output is said to be a preprocessed form of the input data, which is often
Preprocessor
Organized raw data that has not been otherwise processed or transformed
Grouped data are data formed by aggregating individual observations of a variable into groups, so that a frequency distribution of these groups serves
Grouped_data
Statistical concept
statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. Missing data are a common occurrence
Missing_data
Open source distributed database management system
machine learning training and inference functionality as well as data preprocessing and model quality estimation. It natively supports classical training
Apache_Ignite
Data combined from several measurements
data are applied in statistics, data warehouses, and in economics. There is a distinction between aggregate data and individual data. Aggregate data refers
Aggregate_data
American genomic data analysis software
regularly updated analysis and visualization tools (that support data preprocessing, gene expression analysis, proteomics, Single nucleotide polymorphism
GenePattern
Method of statistical inference
hypothesis test is a method of statistical inference used to decide whether the data provide sufficient evidence to reject a particular hypothesis. A statistical
Statistical_hypothesis_test
Data science software
RapidMiner provides data mining and machine learning procedures including: data loading and transformation (ETL), data preprocessing and visualization,
RapidMiner
Type of machine learning model
instructions and to behave as assistants. Biased or inaccurate training data can make an LLM's output less reliable. Benchmark evaluations for LLMs attempt
Large_language_model
Simultaneous observation and analysis of more than one outcome variable
of both how these can be used to represent the distributions of observed data; how they can be used as part of statistical inference, particularly where
Multivariate_statistics
Sequence of data points over time
In mathematics, a time series is a sequence of data points indexed, listed, or graphed in chronological order. Most commonly, a time series consists of
Time_series
complex analysis pipelines. The functionality of the tools ranges from data preprocessing (file format conversion, baseline reduction, noise reduction, peak
The OpenMS Proteomics Pipeline
The_OpenMS_Proteomics_Pipeline
Quantitative analysis of law
algorithms fail to transparently document essential steps, such as data preprocessing, hyperparameter tuning, or the criteria used for splitting training
Jurimetrics
Machine learning technique
normalization (GradNorm) normalizes gradient vectors during backpropagation. Data preprocessing Feature scaling Huang, Lei (2022). Normalization Techniques in Deep
Normalization (machine learning)
Normalization_(machine_learning)
In applied mathematics, a technique to find the shortest path
road networks. The speed-up is achieved by creating shortcuts in a preprocessing phase which are then used during a shortest-path query to skip over
Contraction_hierarchies
Extracting features from raw data for machine learning
learning and statistical modeling, feature engineering is a preprocessing step which transforms raw data into a more effective set of inputs. Each input comprises
Feature_engineering
Free software library
statistical tools for functional, structural and diffusion MRI brain imaging data. FSL is available as both precompiled binaries and source code for Apple
FMRIB_Software_Library
DBMS_PREDICTIVE_ANALYTICS automates the data mining process including data preprocessing, model building and evaluation, and scoring of new data. The PREDICT operation
Oracle_Data_Mining
Integration of multiple data sources to provide better information
Data Fusion Information Group (DFIG) model are: Level 0: Source Preprocessing (or Data Assessment) Level 1: Object Assessment Level 2: Situation Assessment
Data_fusion
Data missing or collected but not analysed
Dark data is data which is acquired through various computer network operations but not used in any manner to derive insights or for decision making. The
Dark_data
Data analysis techniques for fraud detection
data analysis techniques are: Data preprocessing techniques for detection, validation, error correction, and filling up of missing or incorrect data.
Data analysis for fraud detection
Data_analysis_for_fraud_detection
Study of survey methods
of individual units from a population and associated techniques of survey data collection, such as questionnaire construction and methods for improving
Survey_methodology
Distinction between nominal, ordinal, interval and ratio variables
which data can be sorted but still does not allow for a relative degree of difference between them. Examples include, on one hand, dichotomous data with
Level_of_measurement
Selection of data points in statistics
judiciously. Sampling has lower costs and faster data collection compared to a census recording data from the entire population (in many cases, collecting
Sampling_(statistics)
press Huang, J., Li, Y. F., & Xie, M., 2015, An empirical analysis of data preprocessing for machine learning-based software cost estimation, Information and
Machine-dependent_software
statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables. Bowker's test
List of analyses of categorical data
List_of_analyses_of_categorical_data
Engineering applied to artificial intelligence
and real-time streams. This data undergoes cleaning, normalization, and preprocessing, often facilitated by automated data pipelines that manage extraction
Artificial intelligence engineering
Artificial_intelligence_engineering
Data recovery technique
filesystems. The algorithm has three phases: preprocessing, collation, and reassembly. In the preprocessing phase, blocks are decompressed and/or decrypted
File_carving
Branch of statistics
uses the Acute Myelogenous Leukemia survival data set "aml" from the "survival" package in R. The data set is from Miller (1997) and the question is
Survival_analysis
Cryptographic attack
granted real data obtained from a specific unknown key. They then try to use this data with the precomputed table from the preprocessing phase to find
Time/memory/data tradeoff attack
Time/memory/data_tradeoff_attack
Combining of sensor data from disparate sources
preliminary data- or feature level processing. The main goal in decision fusion is to use meta-level classifier while data from nodes are preprocessed by extracting
Sensor_fusion
Psychophysiology manual
Mixed Signals: Preprocessing Psychophysiological Data in Brain Vision Analyzer is a psychophysiology manual for experimental psychologists. Originally
Mixed Signals: Preprocessing Psychophysiological Data in Brain Vision Analyzer
Mixed_Signals:_Preprocessing_Psychophysiological_Data_in_Brain_Vision_Analyzer
Method for discovering interesting relations between variables in databases
David; Feglar, Tomáš (2004). "The GUHA Method, Data Preprocessing and Mining". Database Support for Data Mining Applications. Lecture Notes in Computer
Association_rule_learning
extend beyond model weights. The project has published training data, preprocessing tools, code, training recipes, evaluation tools, and intermediate
OLMo_(language_model)
Mass spectrometry software is used for data acquisition, analysis, or representation in mass spectrometry. In protein mass spectrometry, tandem mass spectrometry
List of mass spectrometry software
List_of_mass_spectrometry_software
Measure of statistical dispersion
(IQR) is a measure of statistical dispersion, which is the spread of the data. The IQR may also be called the midspread, middle 50%, fourth spread, or
Interquartile_range
Type of statistics
aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent. This generally means
Descriptive_statistics
Machine learning model for speech
and 125,000 hours of X→English translation data, where X stands for any non-English language. Preprocessing involved standardization of transcripts, filtering
Whisper (speech recognition system)
Whisper_(speech_recognition_system)
Process of separating populations of cells
designed by data scientists based on intracellular properties. The process includes Single Cell RNA-Seq data gathering; data preprocessing for clustering;
Cell_sorting
Process of using data analysis for predicting population data from sample data
Statistical inference is the process of using data analysis to infer properties of an underlying probability distribution. Inferential statistical analysis
Statistical_inference
Concept in machine learning
(September 2022). "On the Cross-Validation Bias due to Unsupervised Preprocessing". Journal of the Royal Statistical Society Series B: Statistical Methodology
Leakage_(machine_learning)
Searching for patterns in text
be given within constant time. The requirement regarding preprocessing vary: O(m) preprocessing may be allowed after the pattern is read (but before the
String-searching_algorithm
Condition in which the value of a measurement or observation is only partially known
problem of censored data, in which the observed value of some variable is partially known, is related to the problem of missing data, where the observed
Censoring_(statistics)
Middle quantile of a data set or probability distribution
the higher half from the lower half of a data sample, a population, or a probability distribution. For a data set, it may be thought of as the "middle"
Median
Database stored query result set
from data in the database when access to that view is requested. Changes applied to the data in a relevant underlying table are reflected in the data shown
View_(SQL)
Technique for increasing rendering speed in computer graphics
disadvantages are: There are additional storage requirements for the PVS data. Preprocessing times may be long or inconvenient. Can't be used for completely dynamic
Potentially_visible_set
Experiment methodology
quasi-experimental or other non-experimental situations—commonplace with survey data, offline data, and other, more complex phenomena. "A/B testing" is a shorthand for
A/B_testing
General-purpose programming language
significant in C; however, line boundaries do have significance during the preprocessing phase. Comments may appear either between the delimiters /* and */,
C_(programming_language)
Single-cell sequencing technology
Lee I (2020-01-01). "Single-cell ATAC sequencing analysis: From data preprocessing to hypothesis generation". Computational and Structural Biotechnology
ScGET-seq
Data science software
of nodes blending different data sources, including preprocessing (extract, transform, load, or ETL), for modeling, data analysis and visualization with
KNIME
Variable capable of taking on a limited number of possible values
data is the statistical data type consisting of categorical variables or of data that has been converted into that form, for example as grouped data.
Categorical_variable
Method of estimating the parameters of a statistical model, given observations
some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable
Maximum_likelihood_estimation
Compilation of information about a given population
disseminating data on the structure of agriculture, covering the whole or a significant part of a country." "In a census of agriculture, data are collected
Census
Subset of artificial intelligence
also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these
Machine_learning
Statistical relationship
type of statistical relationship between two random variables or bivariate data. It usually refers to the extent to which a pair of quantities are linearly
Correlation
Type of statistics
are often not met in practice. In particular, it is often assumed that the data errors are normally distributed, at least approximately, or that the central
Robust_statistics
Branch of statistics
with the analysis of and inference from data assuming that the underlying distribution, from which the observed data was drawn, can be described by a finite
Parametric_statistics
Digital library portal operated by the Smithsonian
The SAO/NASA Astrophysics Data System (ADS) is a digital library portal for researchers on astronomy and physics, operated for NASA by the Smithsonian
Astrophysics_Data_System
Open-source machine learning system for end-to-end data science lifecycle
builtin functions, and a wealth of new built-in functions for data preprocessing including data cleaning, augmentation and feature engineering techniques
Apache_SystemDS
Possible state of a terminal device in Unix-like systems
are interpreted. In cooked mode data is preprocessed before being given to a program, while raw mode passes the data as-is to the program without interpreting
Terminal_mode
Tree node with two other nodes as descendants
Vishkin (1988) simplified the data structure of Harel and Tarjan, leading to an implementable structure with the same asymptotic preprocessing and query time bounds
Lowest_common_ancestor
Data collection system (DCS) is a computer application that facilitates the process of data collection, allowing specific, structured information to be
Data_collection_system
Approach in data analysis
vital in fintech for fraud prevention. Preprocessing data to remove anomalies can be an important step in data analysis, and is done for a number of reasons
Anomaly_detection
Measure of variation in statistics
standard deviation of a random variable, sample, statistical population, data set or probability distribution is the square root of its variance (the variance
Standard_deviation
Model for generating observable data in probability and statistics
it describes a full data-generating process, a generative model can be used to draw new samples that resemble the observed data, a process often referred
Generative_model
MRI procedure that measures brain activity by detecting associated changes in blood flow
point for analysis. The first part of that analysis is preprocessing. The first step in preprocessing is conventionally slice timing correction. The MR scanner
Functional magnetic resonance imaging
Functional_magnetic_resonance_imaging
Algorithm for finding shortest paths
weights, directed acyclic graphs etc.) can be improved further. If preprocessing is allowed, algorithms such as contraction hierarchies can be up to
Dijkstra's_algorithm
Data visualization
demonstrating graphically the locality, spread and skewness groups of numerical data through their quartiles. In addition to the box on a box plot, there can
Box_plot
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