By Guozhu Dong PhD, Jian Pei PhD (auth.)
Understanding series information, and the power to make use of this hidden wisdom, creates an important effect on many elements of our society. Examples of series information contain DNA, protein, purchaser buy historical past, net browsing background, and more.
Sequence info Mining presents balanced assurance of the prevailing effects on series information mining, in addition to trend kinds and linked trend mining equipment. whereas there are numerous books on information mining and series information research, presently there are not any books that stability either one of those subject matters. This specialist quantity fills within the hole, permitting readers to entry state of the art leads to one place.
Sequence information Mining is designed for pros operating in bioinformatics, genomics, internet providers, and monetary information research. This ebook can be appropriate for advanced-level scholars in computing device technology and bioengineering.
Forward by means of Professor Jiawei Han, college of Illinois at Urbana-Champaign.
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Let l be the length of α. Scan SDB|α once, ﬁnd length-(l + 1) frequent preﬁx in SDB|α , and remove infrequent items and useless sequences; 2. 14) do a) if α satisﬁes C, then output α as a pattern; b) form SDB|α ; c) call pref ix growth(α , SDB|α ) Fig. 4. The Prefix-growth algorithm. Second, Preﬁx-growth handles a broader scope of constraints than antimonotonicity and monotonicity. A typical such example is regular expression constraints, which are diﬃcult to address using an Apriori-based method, as shown in SPIRIT.
2 PreﬁxSpan Let us ﬁrst introduce the concepts of preﬁx and suﬃx which are essential in PreﬁxSpan. 5 (Preﬁx). Suppose all the items within an element are listed alphabetically. For a given sequence α = e1 e2 · · · en , where each ei (1 i n) is an element, a sequence β = e1 e2 · · · em (m n) is called a preﬁx of α if (1) ei = ei for i m − 1; (2) em ⊆ em ; and (3) all items in (em − em ) are alphabetically after those in em . For example, consider sequence s = a(abc)(ac)d(cf ). Sequences a, aa, a(ab) and a(abc) are preﬁxes of s, but neither ab nor a(bc) is a preﬁx.
Techniques for reducing the number of projected databases will be discussed in the next subsection. Theoretically, the problem of mining the complete set of sequential patterns is #P-complete . Therefore, it is impossible to have a polynomial time algorithm unless P = N P . Even if P = N P , it is still unclear whether a polynomial time algorithm exists. Interestingly, we can show that the PreﬁxSpan algorithm is pseudopolynomial. That is, the complexity of PreﬁxSpan is linear with respect to the number of sequential patterns, since each projection generates at least one sequential pattern, and the projection cost is upper bounded by the time of scanning the database once, and counting frequent items in the suﬃxes.