Data compression in data mining

  • Data reduction techniques in data Science

    Data compression is the process of reducing the size of digital data while preserving the essential information contained in them.
    Data can be compressed using algorithms to remove redundancies or irrelevancies in the data, making it simpler to store and more effective to transmit..

  • How data compression is performed in machine learning?

    There is a close connection between machine learning and compression.
    A system that predicts the posterior probabilities of a sequence given its entire history can be used for optimal data compression (by using arithmetic coding on the output distribution)..

  • What is data reduction in data mining?

    Data reduction is a method of reducing the size of original data so that it may be represented in a much smaller space.
    While reducing data, data reduction techniques preserve data integrity.
    The time spent on data reduction should not be overlooked in favour of the time saved by data mining on the smaller data set..

Data Compression Data compression employs modification, encoding, or converting the structure of data in a way that consumes less space. Data compression involves building a compact representation of information by removing redundancy and representing data in binary form.
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.
Data reduction does not necessarily mean loss of information.
For example, the body mass index reduces two dimensions into a single measure, without any information being lost in the process.

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