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  2. Data reduction - Wikipedia

    en.wikipedia.org/wiki/Data_reduction

    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

  3. Active learning (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Active_learning_(machine...

    User Centered Labeling Strategies: Learning is accomplished by applying dimensionality reduction to graphs and figures like scatter plots. Then the user is asked to label the compiled data (categorical, numerical, relevance scores, relation between two instances.

  4. Data deduplication - Wikipedia

    en.wikipedia.org/wiki/Data_deduplication

    In computing, data deduplication is a technique for eliminating duplicate copies of repeating data. Successful implementation of the technique can improve storage utilization, which may in turn lower capital expenditure by reducing the overall amount of storage media required to meet storage capacity needs.

  5. Dimensionality reduction - Wikipedia

    en.wikipedia.org/wiki/Dimensionality_reduction

    The process of feature selection aims to find a suitable subset of the input variables (features, or attributes) for the task at hand.The three strategies are: the filter strategy (e.g., information gain), the wrapper strategy (e.g., accuracy-guided search), and the embedded strategy (features are added or removed while building the model based on prediction errors).

  6. Data dredging - Wikipedia

    en.wikipedia.org/wiki/Data_dredging

    Data dredging (also known as data snooping or p-hacking) [1] [a] is the misuse of data analysis to find patterns in data that can be presented as statistically significant, thus dramatically increasing and understating the risk of false positives.

  7. Data wrangling - Wikipedia

    en.wikipedia.org/wiki/Data_wrangling

    Data wrangling can benefit data mining by removing data that does not benefit the overall set, or is not formatted properly, which will yield better results for the overall data mining process. An example of data mining that is closely related to data wrangling is ignoring data from a set that is not connected to the goal: say there is a data ...

  8. Evolutionary data mining - Wikipedia

    en.wikipedia.org/wiki/Evolutionary_data_mining

    Evolutionary data mining, or genetic data mining is an umbrella term for any data mining using evolutionary algorithms.While it can be used for mining data from DNA sequences, [1] it is not limited to biological contexts and can be used in any classification-based prediction scenario, which helps "predict the value ... of a user-specified goal attribute based on the values of other attributes."

  9. Data transformation (computing) - Wikipedia

    en.wikipedia.org/wiki/Data_transformation...

    Data discovery is the first step in the data transformation process. Typically the data is profiled using profiling tools or sometimes using manually written profiling scripts to better understand the structure and characteristics of the data and decide how it needs to be transformed.