INTRODUCTION TO DATA MINING, 2ND EDITION (GLOBAL EDITION) BY
PANG-NING TAN, MICHAEL STEINBACH, VIPIN KUMAR;ETEXT
ISBN:9780134080284 PUBLISHED BY PEARSON COPYRIGHT© 2019 TEST
19 QUESTIONS
TABLE OF CONTENTS
# TOPIC
1 Apply data preprocessing techniques to improve mining outcomes
2 Analyze and evaluate association patterns using advanced metrics
3 Design and assess classification models with proper validation
4 Interpret cluster validity and anomaly detection results in context
5 Introduction to Data Mining
6 2nd Edition
7 Global Edition
8 by Pang
9 Ning Tan
10 Michael Steinbach
11 Vipin Kumar;eText ISBN
12 9780134080284 published by Pearson Copyright© 2019 Test Bank
13 Foundations of Data Mining
14 Applied Data Mining
15 Advanced Data Mining
16 Data Mining Review
ABSTRACT
Page 1
, This study document brings together 19 carefully worded exam questions drawn from Introduction
to Data Mining, 2nd Edition (Global Edition) by Pang-Ning Tan, Michael Steinbach, Vipin
Kumar;eText ISBN:9780134080284 published by Pearson Copyright© 2019 Test Bank, with the
strongest emphasis placed on Apply data preprocessing techniques to improve mining outcomes,
Analyze and evaluate association patterns using advanced metrics and Design and assess
classification models with proper validation. Every item follows the wording style and level of
reasoning you meet in the real paper, and each one is paired with a clear rationale so the correct
choice is never a guess. Work through the set at your own pace, mark the questions that slow you
down, then come back to them until the reasoning feels automatic. Learners who revise this way
walk into the exam room recognising the pattern behind the questions instead of meeting them for
the first time. Keep going - steady, honest practice is what turns a difficult paper into a comfortable
pass.
Q1 APPLY DATA PREPROCESSING TECHNIQUES TO IMPROVE MINING OUTCOMES
When using z-score normalization on a dataset with a feature that follows a
heavy-tailed distribution, what is the primary drawback compared to robust
normalization methods like median absolute deviation (MAD)?
A. Z-score normalization assumes a uniform distribution, causing poor scaling.
B. Z-score normalization is sensitive to outliers, which can distort the mean and standard
deviation. CORRECT
C. Z-score normalization fails to handle negative values, requiring prior shifting.
D. Z-score normalization only works for categorical data, not continuous features.
RATIONALE: Z-score normalization uses mean and standard deviation, which are highly
influenced by outliers in heavy-tailed distributions, leading to distorted scaling. MAD is robust as
it relies on median and median absolute deviation. Options A, C, and D are incorrect because
z-score does not assume uniformity, handles negative values, and is designed for continuous
data.
Page 2
PANG-NING TAN, MICHAEL STEINBACH, VIPIN KUMAR;ETEXT
ISBN:9780134080284 PUBLISHED BY PEARSON COPYRIGHT© 2019 TEST
19 QUESTIONS
TABLE OF CONTENTS
# TOPIC
1 Apply data preprocessing techniques to improve mining outcomes
2 Analyze and evaluate association patterns using advanced metrics
3 Design and assess classification models with proper validation
4 Interpret cluster validity and anomaly detection results in context
5 Introduction to Data Mining
6 2nd Edition
7 Global Edition
8 by Pang
9 Ning Tan
10 Michael Steinbach
11 Vipin Kumar;eText ISBN
12 9780134080284 published by Pearson Copyright© 2019 Test Bank
13 Foundations of Data Mining
14 Applied Data Mining
15 Advanced Data Mining
16 Data Mining Review
ABSTRACT
Page 1
, This study document brings together 19 carefully worded exam questions drawn from Introduction
to Data Mining, 2nd Edition (Global Edition) by Pang-Ning Tan, Michael Steinbach, Vipin
Kumar;eText ISBN:9780134080284 published by Pearson Copyright© 2019 Test Bank, with the
strongest emphasis placed on Apply data preprocessing techniques to improve mining outcomes,
Analyze and evaluate association patterns using advanced metrics and Design and assess
classification models with proper validation. Every item follows the wording style and level of
reasoning you meet in the real paper, and each one is paired with a clear rationale so the correct
choice is never a guess. Work through the set at your own pace, mark the questions that slow you
down, then come back to them until the reasoning feels automatic. Learners who revise this way
walk into the exam room recognising the pattern behind the questions instead of meeting them for
the first time. Keep going - steady, honest practice is what turns a difficult paper into a comfortable
pass.
Q1 APPLY DATA PREPROCESSING TECHNIQUES TO IMPROVE MINING OUTCOMES
When using z-score normalization on a dataset with a feature that follows a
heavy-tailed distribution, what is the primary drawback compared to robust
normalization methods like median absolute deviation (MAD)?
A. Z-score normalization assumes a uniform distribution, causing poor scaling.
B. Z-score normalization is sensitive to outliers, which can distort the mean and standard
deviation. CORRECT
C. Z-score normalization fails to handle negative values, requiring prior shifting.
D. Z-score normalization only works for categorical data, not continuous features.
RATIONALE: Z-score normalization uses mean and standard deviation, which are highly
influenced by outliers in heavy-tailed distributions, leading to distorted scaling. MAD is robust as
it relies on median and median absolute deviation. Options A, C, and D are incorrect because
z-score does not assume uniformity, handles negative values, and is designed for continuous
data.
Page 2
