By Roberto J. Bayardo (auth.), Jean-François Boulicaut, Luc De Raedt, Heikki Mannila (eds.)
The 18 articles during this cutting-edge survey current the newest leads to inductive querying and constraint-based info mining and in addition determine 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 purposes of inductive querying techniques.
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Additional resources for Constraint-Based Mining and Inductive Databases: European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers
The potential eﬃcient evaluation of the proposed primitive is discussed in Section 4 and 5; here we advocate its adequate expressiveness and versatility showing how it can be embedded in query languages of diﬀerent ﬂavour. Example 14 (Embedding the primitive in a SQL-like language). Consider the constraint-based frequent pattern query of Example 13. The following is some SQL-like syntactic sugar to express such query. MINE PATTERNS Freq_pat, Support FROM sales GROUPING item BY day,cust MINIMUM SUPPORT: 3 CONSTRAINTS ON name FROM product HAVING SUM(price) >= 30, In the ﬁrst line we deﬁne the name of the two output attributes Freq pat and Support corresponding to the variables I and S of the query in Example 13.
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.
Toivonen. Levelwise Search and Border of Theories in Knowledge Discovery. Data Mining and Knowledge Discovery, 3:241–258, 1997. 42. R. Meo, G. Psaila, and S. Ceri. A new SQL-like operator for mining association rules. In Proceedings of VLDB’96. 43. R. Meo, G. Psaila, and S. Ceri. A Tightly-Coupled Architecture for Data Mining. In Proceedings of ICDE’98. 44. R. T. Ng, L. V. S. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained associations rules. In Proceedings of the ACM SIGMOD’98.
Constraint-Based Mining and Inductive Databases: European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers by Roberto J. Bayardo (auth.), Jean-François Boulicaut, Luc De Raedt, Heikki Mannila (eds.)