Get Constraint-Based Mining and Inductive Databases: European PDF

By Jean-Francois Boulicaut, Luc De Raedt, Heikki Mannila

ISBN-10: 3540313311

ISBN-13: 9783540313311

The interconnected rules of inductive databases and constraint-based mining are attractive and feature the aptitude to transform the speculation and perform of knowledge mining and information discovery. This publication reviews at the result of the eu IST undertaking "cInQ" (consortium on wisdom discovery by means of Inductive Queries) and its ultimate workshop entitled Constraint-Based Mining and Inductive Databases organized in Hinterzarten, Germany in March 2004.

The 18 articles during this state of the art survey current the newest ends up in inductive querying and constraint-based information mining and likewise establish destiny instructions of this newly rising box mendacity on the intersection of knowledge mining and database learn. The papers tackle topical sections on foundations of inductive database frameworks, optimizing inductive queries on neighborhood styles, optimizing inductive queries on international styles, and purposes of inductive querying suggestions.

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Additional info for Constraint-Based Mining and Inductive Databases: European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March

Example text

In [48, 49], two FP-growth based algorithms are introduced: F IC A to mine Th(Cfreq ) ∩ Th(CCAM ), and F IC M to mine Th(Cfreq )∩Th(CCM ). A major limitation of any FP-growth based algorithm is that the initial database (internally compressed in the prefix-tree structure) and all intermediate projected databases must fit into main memory. If this requirement cannot be met, these approaches can simply not be applied anymore. This problem is even harder with F IC A and F IC M : in fact, using an order on items different from the frequency-based one, makes the prefixtree lose its compressing power.

Algorithm 3 Constraint-based Frequent Pattern Mining Input: D, σ, C Output: Th(Cfreq [D,σ] ) ∩ Th(C) 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: L1 ← I C1 ← {{i} | i ∈ I ∧ CAMS ({i}) ∧ CAM ({i})} D1 ← πC1 (D) L1 , D1 ← count f irst iteration(D1 , σ, C1 , CM , CMS ) while L1 = C1 do C1 ← L1 ; L1 , D1 ← count f irst iteration(D1 , σ, C1 , CM , CMS ) C2 ← generate(L1 , CAM , CMS ) forall i ∈ L1 do V1 [i] ← 0 k←2 while Ck = ∅ do Lk , Dk+1 , Vk ← count&reduce(Dk , σ, CM , CMS , CLAM , Ck , Vk−1 ) Ck+1 ← generate(Lk , CAM , CMS ) k++ for(i = 0; i ≤ k; i + +) do forall X ∈ Li do if CM (X) ∧ CMS (X) ∧ CLAM (X) then return X Let us briefly describe the pseudo-code in Algorithm 3.

The idea is to generalize ExAnte’s α-reduction from singletons level to the generic level k. This generalization results in the following set of data reduc- 28 F. Bonchi, F. Giannotti, and D. Pedreschi tion techniques, which are based on the anti-monotonicity of Cfreq (see [6] for the proof of correctness). Gk (i): an item which is not subset of at least k frequent k-itemsets can be pruned away from all transactions in D. Tk (t): a transaction which is not superset of at least k + 1 frequent k-itemsets can be removed from D.

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Constraint-Based Mining and Inductive Databases: European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March by Jean-Francois Boulicaut, Luc De Raedt, Heikki Mannila

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