This book provides balanced coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods.

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

Autorentext

Dr. Guozhu Dong is a professor of Computer Science and Engineering, and a member at the Knoesis Center of Excellence, at Wright State University. He received a Ph.D. in Computer Science from the University of Southern California and a B.S. in Mathematics from Shandong Univerity. Before joining Wright State University, he was a faculty member at the University of Melbourne. His research interests span data mining, machine learning, databases, data science, bioinformatics, and artificial intelligence. He co-authored a book on Sequence Data Mining, co-edited two books on Contrast Data Mining and on Feature Engineering, respectively, and authored a book on Exploiting the Power of Group Differences. He is known for his pioneering work and sustained effort on emerging/contrast pattern mining and on the use of such patterns in problem solving. He has published hundreds of papers at major international conferences and in top-rate journals in the fields of data mining and databases. He received several best research paper awards at major data mining conferences. At Wright State University, he was recognized for Excellence in Research in his college. He has served on hundreds of program committees of international conferences, and he has chaired the program committees for several such conferences. He is a senior member of both ACM and IEEE.

Inhalt
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.