Preface
When the idea of writing a book came to my mind, originally the ﬁrst idea wasto focus on so-called semi-structured data, such as sequences, trees and graphs,
which exactly I have been working on, more than other data types, and have moreexpertise. However once I started writing this book, I was getting more interested
in covering the entire machine learning and data mining techniques, which are infact connected to each other in many aspects. In particular, methods for semistructured data sometimes can be similar to or the same as those for more basic
data types. Also I became more interested in, simply speaking, writing a more
introductory book, which can be read even by entry-level machine learners. This
would be because writing this book made me remember my days I started working
on machine learning and also machine learning itself just started its history. I
thus tried writing a book comprehensible for even those who are working on otherﬁelds but have some interest in machine learning and/or data mining and also nostern background knowledge on them. As a result I might have focused more onwhat can be consistently used through diﬀerent approaches and also diﬀerent datatypes. In other words, many (diﬀerently regarded) approaches should have somecommon motivations and ideas behind them, even between those in diﬀerent datatypes. These points are partially already known and written in papers, but not
so much at the level of books, while they must be the things which make people
understand machine learning approaches more clearly. Thus this book covers wide
aspects of machine learning, shedding light on such aspects, which are shared by
diﬀerent methods. This would clarify how machine learning methods are similar
to or diﬀerent from each other.
This book is not only for researchers in the relevant ﬁelds but also a wide
variety of people including students and engineers who are interested in artiﬁcial
intelligence or more widely data science, regardless of their background. I hopethat this book is helpful for a lot of readers to have some clearer picture of machinelearning and data mining.
I started working on machine learning when I was working with a company,
where at that time probably the most ﬂexible place in Japan to start doing newresearch. The manager of the department I belonged was open-minded and had agreat interest in artiﬁcial intelligence, thinking that the area is very promising in
the future. I was in the “machine learning” group of the department, and people
there, including me, were strongly encouraged by him to embark upon the newresearch area. I would like to thank him and people in the department for their

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support of my doing machine learning research.
After I moved to academia, I have enjoyed more freedom to do research. Thatis, I could do research, simply following my own preference. The university system
is rather bureaucratic as well as other organizations in Japan, while in the Institutefor Chemical Research, Kyoto University, thanks to my colleagues and particularly
warm arrangement of senior professors, I have been comfortably working in theinstitute. Without the atmosphere of respecting unique, original ways of thinking
in the institute, I did not come to the idea of writing a book.
I started writing this book when I was staying at and working closely withthe Department of Computer Science, Aalto University, Finland. I would like tothank all colleagues and people in the department. Aalto University welcomed me
warmly and kindly, oﬀering all I need to do research and other work. The systemof the department, school, university and even country are all well organized,
also always exploring eﬃciency and its improvement. I’d like to thank people
who established and are maintaining those systems. This book was not realized
without all eﬃcient systems of the country.
In this book, part of the research the author has conducted with colleagues
are introduced. Performing the collaborative research has not been done without the tremendous eﬀort by the collaborators. Particularly in those work, a lotof missing parts of the author’s ability were complemented by the excellent insights and capability of the collaborators. I deeply thank those collaborators I
raise below alphabetically: Naoki Abe, Kiyoko Aoki-Kinoshita, David duVerle,Timothy Hancock, Kosuke Hashimoto, Minoru Kanehisa, Masayuki Karasuyama,
Atsuyoshi Nakamura, Canh Hao Nguyen, Yasuko Ono, Motoki Shiga, Hiroyuki
Sorimachi, Ichigaku Takigawa, Koji Tsuda, Keiko Udaka, Nobuhisa Ueda, Raymond Wan, Takashi Yoneya, Sohiya Yotsukura and Shanfeng Zhu.

Hiroshi Mamitsuka
Uji, Japan / Espoo, Finland
August, 2018.