Home Structured Design • Download PDF by Jean-Francois Boulicaut, Luc De Raedt, Heikki Mannila: Constraint-Based Mining and Inductive Databases: European

Download PDF by Jean-Francois Boulicaut, Luc De Raedt, Heikki Mannila: Constraint-Based Mining and Inductive Databases: European

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

ISBN-10: 3540313311

ISBN-13: 9783540313311

The interconnected principles of inductive databases and constraint-based mining are attractive and feature the aptitude to seriously change the speculation and perform of information mining and data discovery. This publication experiences at the result of the ecu 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 leads to inductive querying and constraint-based facts mining and likewise establish destiny instructions of this newly rising box mendacity on the intersection of knowledge mining and database examine. The papers deal with topical sections on foundations of inductive database frameworks, optimizing inductive queries on neighborhood styles, optimizing inductive queries on international styles, and functions of inductive querying strategies.

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

Example text

There is no unique element that is the upper bound for this set. 5 Pattern Mining After defining tree matching notions and therewith generality relations over L, we can formalize the frequent tree mining problem. As stated before, let L be a formal language composed of all possible labeled, ordered, rooted trees and D ⊆ L a database. To count the trees t ∈ D containing a pattern p with regard to a matching notion χ we define a function dt,χ : L → {0, 1} as dt,χ (p) =def 1 if p χ t 0 otherwise, (3) that is 1 if the pattern p occurs at least once in the tree t, and 0 otherwise.

19. L. De Raedt and S. Kramer. The levelwise version space algorithm and its application to molecular fragment finding. In Proceedings of IJCAI’01. 20. S. Dˇzeroski and N. Lavraˇc, editors. Relational Data Mining. Springer, Berlin, 2001. 21. F. Giannotti and G. Manco. Querying Inductive Databases via Logic-Based UserDefined Aggregates. In Proceedings of PKDD’99. 36 F. Bonchi, F. Giannotti, and D. Pedreschi 22. F. Giannotti and G. Manco. Making Knowledge Extraction and Reasoning Closer. In T. Terano, editor, Proceedings of PAKDD’00.

Hence, a tree p embedded in a tree t is also incorporated in t. Proof (matchicpr → matchsub ): Given an injective mapping ϕ from Vp to the nodes of Vt , such that ∀u, v ∈ Vp (λ(u) = λ(ϕ(u))) ∧ (u ≺ v ⇐ ϕ(u) ≺ ϕ(v)) ∧ (v ∈ π ∗ (u) ⇒ ϕ(v) ∈ π ∗ (ϕ(u))), it is obvious that only the part of the conditions required by tree subsumption will hold as well. Thus, if there exists a mapping ϕ such that a tree p is incorporated in a tree t it is also subsumed by t. 4 Generality On each of the matching notions, a more-general than (more-specific than) relation ( ) can be defined.

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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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