Solution Manual for Data Mining and Machine Learning Fundamental Concepts and Algorithms 2nd Edition
Zaki, Wagner Meira Jr
,Contents
Contents 1
1 Data Mining and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.7 Exercises 3
PART I DATA ANALYSIS FOUNDATIONS 5
2 Numeric Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.7 Exercises 7
3 Categorical Attributes .............................................................................................. 16
3.7 Exercises 16
4 Graph Data ................................................................................................................. 20
4.6 Exercises 20
5 Kernel Methods ......................................................................................................... 26
5.6 Exercises 26
6 High-dimensional Data .............................................................................................. 29
6.9 Exercises 29
7 Dimensionality Reduction ....................................................................................... 39
7.6 Exercises 39
PART II FREQUENT PATTERN MINING 45
8 Itemset Mining ........................................................................................................... 47
8.5 Exercises 47
9 Summarizing Itemsets .............................................................................................. 56
9.6 Exercises 56
1
,Solution Manual for Data Mining and Machine Learning Fundamental Concepts and
Algorithms 2nd Edition by Mohammed J. Zaki, Wagner Meira Jr
10 Sequence Mining....................................................................................................... 63
10.5 Exercises 63
11 Graph Pattern Mining ................................................................................................ 75
11.5 Exercises 75
12 Pattern and Rule Assessment................................................................................... 84
12.4 Exercises 84
PART III CLUSTERING 89
13 Representative-based Clustering ............................................................................. 91
13.5 Exercises 91
14 Hierarchical Clustering ............................................................................................ 99
14.4 Exercises 99
15 Density-based Clustering ........................................................................................ 106
15.5 Exercises 106
16 Spectral and Graph Clustering ............................................................................... 111
16.5 Exercises 111
17 Clustering Validation .............................................................................................. 118
17.5 Exercises 118
PART IV CLASSIFICATION 123
18 Probabilistic Classification ..................................................................................... 125
18.5 Exercises 125
19 Decision Tree Classifier........................................................................................... 129
19.4 Exercises 129
20 Linear Discriminant Analysis .................................................................................. 137
20.4 Exercises 137
21 Support Vector Machines........................................................................................ 141
21.7 Exercises 141
22 Classification Assessment..................................................................................... 145
22.5 Exercises 145
This book has been published by Cambridge University Press. No unauthorized distribution shall be allowed.
, CHAP T ER 1 Data Mining and Analysis
1.7 EXERCISES
Q1. Show that the mean of the centered data matrix Z in Eq. (1.5) is 0.
1 1
(xi — µ)
1 1
—
— 0
Q2. Prove that for the Lp-distance in Eq. (1.2), we have
δ (x, y) = lim δ (x, y) = d |x — y |}
∞ p max i i
p→∞ i=1
for x, y e Rd.
Answer: We have to show that
d
d
lim x |x — y |
assume that |xi — yi | < m for all i /= a.
d
|x — y | |x — y |
m
m m
i/=a
3
Zaki, Wagner Meira Jr
,Contents
Contents 1
1 Data Mining and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.7 Exercises 3
PART I DATA ANALYSIS FOUNDATIONS 5
2 Numeric Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.7 Exercises 7
3 Categorical Attributes .............................................................................................. 16
3.7 Exercises 16
4 Graph Data ................................................................................................................. 20
4.6 Exercises 20
5 Kernel Methods ......................................................................................................... 26
5.6 Exercises 26
6 High-dimensional Data .............................................................................................. 29
6.9 Exercises 29
7 Dimensionality Reduction ....................................................................................... 39
7.6 Exercises 39
PART II FREQUENT PATTERN MINING 45
8 Itemset Mining ........................................................................................................... 47
8.5 Exercises 47
9 Summarizing Itemsets .............................................................................................. 56
9.6 Exercises 56
1
,Solution Manual for Data Mining and Machine Learning Fundamental Concepts and
Algorithms 2nd Edition by Mohammed J. Zaki, Wagner Meira Jr
10 Sequence Mining....................................................................................................... 63
10.5 Exercises 63
11 Graph Pattern Mining ................................................................................................ 75
11.5 Exercises 75
12 Pattern and Rule Assessment................................................................................... 84
12.4 Exercises 84
PART III CLUSTERING 89
13 Representative-based Clustering ............................................................................. 91
13.5 Exercises 91
14 Hierarchical Clustering ............................................................................................ 99
14.4 Exercises 99
15 Density-based Clustering ........................................................................................ 106
15.5 Exercises 106
16 Spectral and Graph Clustering ............................................................................... 111
16.5 Exercises 111
17 Clustering Validation .............................................................................................. 118
17.5 Exercises 118
PART IV CLASSIFICATION 123
18 Probabilistic Classification ..................................................................................... 125
18.5 Exercises 125
19 Decision Tree Classifier........................................................................................... 129
19.4 Exercises 129
20 Linear Discriminant Analysis .................................................................................. 137
20.4 Exercises 137
21 Support Vector Machines........................................................................................ 141
21.7 Exercises 141
22 Classification Assessment..................................................................................... 145
22.5 Exercises 145
This book has been published by Cambridge University Press. No unauthorized distribution shall be allowed.
, CHAP T ER 1 Data Mining and Analysis
1.7 EXERCISES
Q1. Show that the mean of the centered data matrix Z in Eq. (1.5) is 0.
1 1
(xi — µ)
1 1
—
— 0
Q2. Prove that for the Lp-distance in Eq. (1.2), we have
δ (x, y) = lim δ (x, y) = d |x — y |}
∞ p max i i
p→∞ i=1
for x, y e Rd.
Answer: We have to show that
d
d
lim x |x — y |
assume that |xi — yi | < m for all i /= a.
d
|x — y | |x — y |
m
m m
i/=a
3
