ALL 13 CHAPTERS COVERED
,Data Mining: Concepts and
Techniques
3rd Edition
Solution Manual
Jiawei Han, Micheline Kamber, Jian Pei
The University of Illinois at Urbana-Champaign
Simon Fraser University
Version January 2, 2012
⃝Morgan
c Kaufmann, 2011
For Instructors’ references only.
Do not copy! Do not distribute!
,ii
, Preface
For a rapidly evolving field like data mining, it is difficult to compose “typical” exercises and even more
difficult to work out “standard” answers. Some of the exercises in Data Mining: Concepts and Techniques
are themselves good research topics that may lead to future Master or Ph.D. theses. Therefore, our solution
manual is intended to be used as a guide in answering the exercises of the textbook. You are welcome to
enrich this manual by suggesting additional interesting exercises and/or providing more thorough, or better
alternative solutions.
While we have done our best to ensure the correctness of the solutions, it is possible that some typos or
errors may exist. If you should notice any, please feel free to point them out by sending your suggestions to
. We appreciate your suggestions.
To assist the teachers of this book to work out additional homework or exam questions, we have added
one additional section “Supplementary Exercises” to each chapter of this manual. This section includes
additional exercise questions and their suggested answers and thus may substantially enrich the value of this
solution manual. Additional questions and answers will be incrementally added to this section, extracted
from the assignments and exam questions of our own teaching. To this extent, our solution manual will be
incrementally enriched and subsequently released in the future months and years.
Notes to the current release of the solution manual.
Due to the limited time, this release of the solution manual is a preliminary version. Many of the newly
added exercises in the third edition have not provided the solutions yet. We apologize for the inconvenience.
We will incrementally add answers to those questions in the next several months and release the new versions
of updated solution manual in the subsequent months.
Acknowledgements
For each edition of this book, the solutions to the exercises were worked out by different groups of teach
assistants and students. We sincerely express our thanks to all the teaching assistants and participating
students who have worked with us to make and improve the solutions to the questions. In particular, for the
first edition of the book, we would like to thanks Denis M. C. Chai, Meloney H.-Y. Chang, James W. Herdy,
Jason W. Ma, Jiuhong Xu, Chunyan Yu, and Ying Zhou who took the class of CMPT-459: Data Mining and
Data Warehousing at Simon Fraser University in the Fall semester of 2000 and contributed substantially to
the solution manual of the first edition of this book. For those questions that also appear in the first edition,
the answers in this current solution manual are largely based on those worked out in the preparation of the
first edition.
For the solution manual of the second edition of the book, we would like to thank Ph.D. students and
teaching assistants, Deng Cai and Hector Gonzalez, for the course CS412: Introduction to Data Mining and
Data Warehousing, offered in the Fall semester of 2005 in the Department of Computer Science at the
University of Illinois at Urbana-Champaign. They have helped prepare and compile the answers for the new
exercises of the first seven chapters in our second edition. Moreover, our thanks go to several students from
the CS412 class in the Fall semester of 2005 and the CS512: Data Mining: Principles and Algorithms classes
iii
,Data Mining: Concepts and
Techniques
3rd Edition
Solution Manual
Jiawei Han, Micheline Kamber, Jian Pei
The University of Illinois at Urbana-Champaign
Simon Fraser University
Version January 2, 2012
⃝Morgan
c Kaufmann, 2011
For Instructors’ references only.
Do not copy! Do not distribute!
,ii
, Preface
For a rapidly evolving field like data mining, it is difficult to compose “typical” exercises and even more
difficult to work out “standard” answers. Some of the exercises in Data Mining: Concepts and Techniques
are themselves good research topics that may lead to future Master or Ph.D. theses. Therefore, our solution
manual is intended to be used as a guide in answering the exercises of the textbook. You are welcome to
enrich this manual by suggesting additional interesting exercises and/or providing more thorough, or better
alternative solutions.
While we have done our best to ensure the correctness of the solutions, it is possible that some typos or
errors may exist. If you should notice any, please feel free to point them out by sending your suggestions to
. We appreciate your suggestions.
To assist the teachers of this book to work out additional homework or exam questions, we have added
one additional section “Supplementary Exercises” to each chapter of this manual. This section includes
additional exercise questions and their suggested answers and thus may substantially enrich the value of this
solution manual. Additional questions and answers will be incrementally added to this section, extracted
from the assignments and exam questions of our own teaching. To this extent, our solution manual will be
incrementally enriched and subsequently released in the future months and years.
Notes to the current release of the solution manual.
Due to the limited time, this release of the solution manual is a preliminary version. Many of the newly
added exercises in the third edition have not provided the solutions yet. We apologize for the inconvenience.
We will incrementally add answers to those questions in the next several months and release the new versions
of updated solution manual in the subsequent months.
Acknowledgements
For each edition of this book, the solutions to the exercises were worked out by different groups of teach
assistants and students. We sincerely express our thanks to all the teaching assistants and participating
students who have worked with us to make and improve the solutions to the questions. In particular, for the
first edition of the book, we would like to thanks Denis M. C. Chai, Meloney H.-Y. Chang, James W. Herdy,
Jason W. Ma, Jiuhong Xu, Chunyan Yu, and Ying Zhou who took the class of CMPT-459: Data Mining and
Data Warehousing at Simon Fraser University in the Fall semester of 2000 and contributed substantially to
the solution manual of the first edition of this book. For those questions that also appear in the first edition,
the answers in this current solution manual are largely based on those worked out in the preparation of the
first edition.
For the solution manual of the second edition of the book, we would like to thank Ph.D. students and
teaching assistants, Deng Cai and Hector Gonzalez, for the course CS412: Introduction to Data Mining and
Data Warehousing, offered in the Fall semester of 2005 in the Department of Computer Science at the
University of Illinois at Urbana-Champaign. They have helped prepare and compile the answers for the new
exercises of the first seven chapters in our second edition. Moreover, our thanks go to several students from
the CS412 class in the Fall semester of 2005 and the CS512: Data Mining: Principles and Algorithms classes
iii
