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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Question

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
set to be
Data
(www.ics.uci.edu/_mlearn/MLRepository.html), as
discretized. Perform data discretization for each of the four numeric
a
data
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.

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