Sequence Data Mining

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Springer Science & Business Media, 2007 M10 31 - 150 pages

Understanding sequence data, and the ability to utilize this hidden knowledge, will create a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more.

This book provides thorough coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. It offers balanced coverage on data mining and sequence data analysis, allowing readers to access the state-of-the-art results in one place.

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Frequent and Closed Sequence Patterns
Classification Clustering Features and Distances of Sequence Data
Identifying and Characterizing Sequence Families
Mining Partial Orders from Sequences
Distinguishing Sequence Patterns
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Page 144 - J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth.
Page 65 - K'(G) of a graph G is the minimum number of edges whose removal from G results in a disconnected graph or a trivial graph.
Page 139 - A. Bateman, E. Birney, L. Cerruti, R. Durbin, L. Etwiller, SR Eddy, S. GriffithsJones, KL Howe, M. Marshall, and ELL Sonnhammer. The Pfam protein families database.
Page 144 - Nucleotide sequence of an RNA polymerase binding site at an early T7 promoter, Proc.
Page 25 - Table 2. 4. The set of sequential patterns is the collection of patterns found in the above recursive mining process. One can verify that it returns exactly the same set of sequential patterns as what GSP and FreeSpan do.
Page 16 - That is, element (x) is written as x. An item can occur at most once in an element of a sequence, but can occur multiple times in different elements of a sequence. The number of instances of items in a sequence is called the length of the sequence. A sequence with length / is called an /-sequence. A sequence a...

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