By Jean-Francois Boulicaut, Luc De Raedt, Heikki Mannila
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 data discovery. This ebook stories 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 cutting-edge survey current the newest ends up in inductive querying and constraint-based information mining and likewise determine destiny instructions of this newly rising box mendacity on the intersection of information 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 concepts.
Read Online or Download Constraint-Based Mining and Inductive Databases: European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March PDF
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Extra info for Constraint-Based Mining and Inductive Databases: European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March
A rooted tree is a directed graph) and every node has exactly one incoming edge, except one designated node v0 called root, which has no incoming edge. Nodes that have no outgoing edges are called leaves. Every node that is not a leaf is an inner node. In a rooted tree, a node c is called a child node of p if (p, c) ∈ E. Dually, p is called parent of c, denoted as p = π(c). If there is a path from a node a to a node d, a is called an ancestor of d, and d is called a descendant of a. Hence, the root node of a tree is an ancestor of all other nodes in the tree.
T. Ng, J. Han, and A. Pang. Optimization of constrained frequent set queries with 2-variable constraints. SIGMOD Record, 28(2), 1999. 39. W. Li, J. Han, and J. Pei. CMAR: Accurate and eﬃcient classiﬁcation based on multiple class-association rules. In In Proceedings of ICDM’01. 40. B. Liu, W. Hsu, and Y. Ma. Integrating classiﬁcation and association rule mining. In Proceedings of KDD’98. 41. H. Mannila and H. Toivonen. Levelwise Search and Border of Theories in Knowledge Discovery. Data Mining and Knowledge Discovery, 3:241–258, 1997.
Constraints that are neither succinct nor anti-monotone are pushed in the CAP  computation by inducing weaker constraints which are either anti-monotone and/or succinct. 2 Monotone Constraints Monotone constraints work the opposite way of anti-monotone constraints. Given an itemset X, a constraint CM is monotone if: ∀Y ⊇ X : CM (X) ⇒ CM (Y ). Since the frequent itemset computation is geared on Cfreq , which is anti-monotone, CM constraints have been considered more hard to be pushed in the computation and less eﬀective in pruning the search space.