Frequent and Closed Sequence Patterns.- Classification, Clustering, Features and Distances of Sequence Data.- Sequence Motifs: Identifying and Characterizing Sequence Families.- Mining Partial Orders from Sequences.- Distinguishing Sequence Patterns.- Related Topics.

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.  

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)

Unlike Dong and Pei’s version of Sequence Data Mining, current books do not provide thorough coverage on this topic — other books either focus on general data mining, on sequence analysis, or are restricted to results of the authors only Includes both topics: frequent/closed sequence patterns and similarity sequence patterns and motifs