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Understanding sequence data, and the ability to utilize this hidden knowledge, creates a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more.
Sequence Data Mining provides balanced coverage of the existing results on sequence data mining, as well as pattern types and associated pattern mining methods. While there are several books on data mining and sequence data analysis, currently there are no books that balance both of these topics. This professional volume fills in the gap, allowing readers to access state-of-the-art results in one place.
Sequence Data Mining is designed for professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for advanced-level students in computer science and bioengineering.
Forward by Professor Jiawei Han, University of Illinois at Urbana-Champaign.
Sequence Data Mining provides balanced coverage of the existing results on sequence data mining, as well as pattern types and associated pattern mining methods. While there are several books on data mining and sequence data analysis, currently there are no books that balance both of these topics. This professional volume fills in the gap, allowing readers to access state-of-the-art results in one place.
Sequence Data Mining is designed for professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for advanced-level students in computer science and bioengineering.
Forward by Professor Jiawei Han, University of Illinois at Urbana-Champaign.
From the reviews:
"In this short book, Dong and Pei provide a good introductory to the topic, organized into seven chapters. ... This book should appeals to researchers and graduate students working in the field (or with an interest in DM) who want to extend their knowledge of sequence DM." (John Fulcher, Computing Reviews, January, 2008)
"In this short book, Dong and Pei provide a good introductory to the topic, organized into seven chapters. ... This book should appeals to researchers and graduate students working in the field (or with an interest in DM) who want to extend their knowledge of sequence DM." (John Fulcher, Computing Reviews, January, 2008)
Aus den Rezensionen:
"Einem kurzen, einleitenden Überblick lassen die Autoren auf dem Fuße schwierige Details folgen: Mathematische Definitionen und Algorithmen erläutern sie zwar immer wieder auch anhand von Beispielen ... In den vier Hauptkapiteln greifen sie spezifische Probleme ... Mehrere hundert wissenschaftliche Referenzen sowie ein lndex runden das Buch schließlich ab. Das kompakte Werk eignet sich ... für Studenten der Informatik oder verwandter Studiengebiete, die einen fundierten Überblick über die praktischen Anwendungsgebiete des Data Mining bekommen wollen ..."
(Tobias Engler/fm, in: c't magazin für computer technik, 2008, Issue 1, S. 192)
"Einem kurzen, einleitenden Überblick lassen die Autoren auf dem Fuße schwierige Details folgen: Mathematische Definitionen und Algorithmen erläutern sie zwar immer wieder auch anhand von Beispielen ... In den vier Hauptkapiteln greifen sie spezifische Probleme ... Mehrere hundert wissenschaftliche Referenzen sowie ein lndex runden das Buch schließlich ab. Das kompakte Werk eignet sich ... für Studenten der Informatik oder verwandter Studiengebiete, die einen fundierten Überblick über die praktischen Anwendungsgebiete des Data Mining bekommen wollen ..."
(Tobias Engler/fm, in: c't magazin für computer technik, 2008, Issue 1, S. 192)