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  2. Data sanitization - Wikipedia

    en.wikipedia.org/wiki/Data_sanitization

    One type of data sanitization is rule based PPDM, which uses defined computer algorithms to clean datasets. Association rule hiding is the process of data sanitization as applied to transactional databases. [32] Transactional databases are the general term for data storage used to record transactions as organizations conduct their business.

  3. Data cleansing - Wikipedia

    en.wikipedia.org/wiki/Data_cleansing

    Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database. It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [ 1 ]

  4. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    The datasets are classified, based on the licenses, as Open data and Non-Open data. The datasets from various governmental-bodies are presented in List of open government data sites. The datasets are ported on open data portals. They are made available for searching, depositing and accessing through interfaces like Open API. The datasets are ...

  5. High frequency data - Wikipedia

    en.wikipedia.org/wiki/High_Frequency_Data

    Data cleaning, or data cleansing, is the process of utilizing algorithmic functions to remove unnecessary, irrelevant, and incorrect data from high frequency data sets. [6] Ultra-high frequency data analysis requires a clean sample of records to be useful for study.

  6. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  7. Data preparation - Wikipedia

    en.wikipedia.org/wiki/Data_preparation

    Data should be consistent between different but related data records (e.g. the same individual might have different birthdates in different records or datasets). Where possible and economic, data should be verified against an authoritative source (e.g. business information is referenced against a D&B database to ensure accuracy). [3] [4]

  8. Data curation - Wikipedia

    en.wikipedia.org/wiki/Data_curation

    The user, rather than the database itself, typically initiates data curation and maintains metadata. [8] According to the University of Illinois' Graduate School of Library and Information Science, "Data curation is the active and on-going management of data through its lifecycle of interest and usefulness to scholarship, science, and education; curation activities enable data discovery and ...

  9. Category:Datasets in machine learning - Wikipedia

    en.wikipedia.org/wiki/Category:Datasets_in...

    Training, validation, and test data sets This page was last edited on 5 May 2023, at 21:06 (UTC). Text is available under the Creative Commons Attribution ...