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Author: Admin | 2025-04-28
Home » Data Mining In this tutorial, we will learn about the data discretization in data mining, why discretization is important, etc. By Palkesh Jain Last updated : April 17, 2023 What is Data discretization? Data discretization is characterized as a method of translating attribute values of continuous data into a finite set of intervals with minimal information loss. Data discretization facilitates the transfer of data by substituting interval marks for the values of numeric data. Similar to the values for the 'generation' variable, interval labels such as (0-10, 11-20...) or (0-10, 11-20...) may be substituted (kid, youth, adult, senior). Data discretization can be divided into two forms of supervised discretization in which the class data is used and the other is unsupervised discretization depending on which way the operation proceeds, i.e. 'top-down splitting strategy' or 'bottom-up merging strategy.' Continuous attributes are something that many real-world data mining tasks need. However, many of the latest exploratory data mining techniques struggle to appeal to such attributes. Besides, even though the machine learning task is able to manage a continuous attribute, its output will benefit greatly if the continuous attributes are replaced with their quantized values. Data discretization is a process of translating continuous data into intervals and then assigning the specific value within this interval. It can also be defined as discretizing time calculated by time interval units, rather than a specific value. Each discrete interval of the discretized attribute domain does not have to contain the discrete values from the discrete attribute domain, but these discrete values do have to induce an ordering on the domain of the discrete attribute itself. As a result, it increases the consistency of discovered information very significantly and also decreases the running time of various data mining tasks such as association rule discovery, classification,
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