3) ChiMerge [Ker92] is a supervised, bottom-up (i.e., merge-based) data discretization method. It relies on _2 analysis: Adjacent intervals with the least _2 values are merged together until the chosen stopping criterion satisfies. (a) Briefly describe how ChiMerge works. (b) Take the IRIS data set, obtained from the University of California-Irvine Machine Learning Repository a data set to be Data (www.ics.uci.edu/_mlearn/MLRepository.html), as discretized. Perform data discretization for each of the four numeric attributes using the ChiMerge method. (Let the stopping criteria be: max- interval D 6). You need to write a small program to do this to avoid clumsy numerical computation. Submit your simple analysis and your test results: split-points, final intervals, and the documented source program.
3) ChiMerge [Ker92] is a supervised, bottom-up (i.e., merge-based) data discretization method. It relies on _2 analysis: Adjacent intervals with the least _2 values are merged together until the chosen stopping criterion satisfies. (a) Briefly describe how ChiMerge works. (b) Take the IRIS data set, obtained from the University of California-Irvine Machine Learning Repository a data set to be Data (www.ics.uci.edu/_mlearn/MLRepository.html), as discretized. Perform data discretization for each of the four numeric attributes using the ChiMerge method. (Let the stopping criteria be: max- interval D 6). You need to write a small program to do this to avoid clumsy numerical computation. Submit your simple analysis and your test results: split-points, final intervals, and the documented source program.
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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