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Data Mining with Application
Homework 6 Naive Bayes Classification, Rule-Based Algorithms, and
K Nearest Neighbors (Chapters 3 & 4)
1. Textbook p.122, Chapter 4, Problem 9 (Naive Bayes, NB): Redo Example 4.5 on p. 87
using Output2 data.
2. Textbook p.122, Chapter 4, Problem 17 (Rule-Based PRISM): Complete Example 4.12 on
pp. 117-118 by generating rules for the short and medium classes.
3. Use KNN (K Nearest Neighbors, on pp. 90-92) to classify <John, M, 2.5> with K=5 using
both the gender and the height attributes of the height data on p.78 of the textbook and
assuming that Output2 is correct. Assume that M = 0 and F = 1.
(Practice: Classify <Lisa, F, 1.75>)
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Table 4.1 Data for Height Classification (p. 78)
Name
Gender Height Output1 Output2
Kristina
F
1.6 m
Short
Medium
Jim
M
2m
Tall
Medium
Maggie
F
1.9 m
Medium Tall
Martha
F
1.88 m Medium Tall
Stephanie F
1.7 m
Short
Medium
Bob
M
1.85 m Medium Medium
Kathy
F
1.6 m
Short
Medium
Dave
M
1.7 m
Short
Medium
Worth
M
2.2 m
Tall
Tall
Steven
M
2.1 m
Tall
Tall
Debbie
F
1.8 m
Medium Medium
Todd
M
1.95 m Medium Medium
Kim
F
1.9 m
Medium Tall
Amy
F
1.8 m
Medium Medium
Wynette
F
1.75 m Medium Medium
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