By Guozhu Dong PhD, Jian Pei PhD (auth.)
Understanding series facts, and the power to make use of this hidden wisdom, creates an important influence on many points of our society. Examples of series info contain DNA, protein, consumer buy background, internet browsing heritage, and more.
Sequence information Mining presents balanced insurance of the prevailing effects on series information mining, in addition to trend kinds and linked trend mining equipment. whereas there are numerous books on facts mining and series info research, at present there aren't any books that stability either one of those subject matters. This expert quantity fills within the hole, permitting readers to entry cutting-edge ends up in one place.
Sequence facts Mining is designed for pros operating in bioinformatics, genomics, net companies, and fiscal information research. This publication can also be appropriate for advanced-level scholars in desktop technological know-how and bioengineering.
Forward via Professor Jiawei Han, college of Illinois at Urbana-Champaign.
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Extra info for Sequence Data Mining
Example text
For example, in sequence (ef )(ab)(df )cb, only the subsequence ( b)(df )cb should be considered for mining sequential patterns prefixed with a. Notice that ( b) means that the last element in the prefix, which is a, together with b, form one element. The sequences in S containing a are projected with respect to a to form the a-projected database, which consists of four suffix sequences: (abc)(ac)d(cf ), ( d)c(bc)(ae), ( b)(df )cb and ( f )cbc. By scanning the a-projected database once, its locally frequent items are a : 2, b : 4, b : 2, c : 4, d : 2, and f : 2.
3 PrefixSpan: A Pattern-growth, Depth-first Search Method 21 method may generate a large set of candidate sequences even for a moderate seed set. For example, if there are 1, 000 length-1 sequential patterns a1 , a2 , . . , a1000 , an Apriori-like algorithm will generate 1000 × 1000 + • • 1000 × 999 = 1, 499, 500 2 length-2 candidate sequences. (The first term is derived from the set of candidate sequences {a1 a1 , a1 a2 , . . , a1 a1000 , a2 a1 , a2 a2 , . . , a1000 a1000 }, and the second term is derived from the set of candidate sequences {(a1 a2 ), (a1 a3 ), .
The above recursive partitioning of the sequential pattern mining problem forms a divide-and-conquer framework. The above partitioning process can be visualized as a sequence enumeration tree . 8 (Sequence enumeration tree). Let the set of items I = {a, b, c, d}. 2 shows a sequence enumeration tree which enumerates all possible sequences formed using the items. <> a b (ab) (ac) (ad) aa ab ac ad ... (abc) (abd) (ab)a (ab)b (ab)c ... ... ... (bc) (bd) ba bb ... c d ... ... ... Fig. 2. The sequence enumeration tree on the set of items {a, b, c, d}.