De Raedt, L., Blockeel, H., Dehaspe, L., & Laer, W. V. (2001). Three Companions for Data Mining in First Order Logic. In S. D zeroski & N. Lavra c (Eds.), Relational Data Mining. Springer-Verlag. Home/Search Document Details and Download
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Using Constraints in Relational Subgroup Discovery - Zelezny, Lavrac, Dzeroski (Correct)
....Introduction Developments in descriptive induction [21, 29] have recently gained much attention of researchers developing rule learning algorithms. These involve mining of association rules (e.g. the APRIORI association rule learning algorithm [1] clausal discovery (e.g. the CLAUDIEN system [21, 22]) subgroup discovery (e.g. the MIDOS [27, 28] and EXPLORA [12] subgroup discovery systems) and other approaches to non classificatory induction aimed at finding interesting patterns in data. In this paper, we consider the task of subgroup discovery: given a population of individuals and a ....
Luc De Raedt, Hendrik Blockeel, Luc Dehaspe, and Wim Van Laer. Three companions for data mining in first order logic. In Saso Dzeroski and Nada Lavrac, editors, Relational Data Mining, pages 105-- 139. Springer-Verlag, 2001.
MRDTL: A multi-relational decision tree learning algorithm - Leiva (2002) (1 citation) (Correct)
....tree induction (e.g. Quinlan s C5 (Quinlan, Rulequest) On the other hand, the goal in descriptive data mining is to characterize as much as possible, by finding patterns regularities, the given examples. Discovering of association rules is one example of this class of data mining techniques (De Raedt et al. 2001). There exist other analytical tools that explore data interrelationships, for instance OLAP (On Line Analytical Processing) But the difference between KDD and these tools is in the approach they use. Many of the analytical tools available support a verification based approach, in which the user ....
.... attributes (feature construction) or to enlarge the hypothesis space by allowing tests involving multiple attributes (e.g. attributel attribute2) Blockeel, 1998) Finally, there are domains where reasoning about the structure of the objects and relations between them is inherently required (De Raedt et el. 2001). For example, Blockeel identifies a chemical database that describes molecules (molecules table) by listing the atoms (atoms table) and bonds (bonds table) that occur in them and using Mendelev s periodic table of elements as background knowledge (mendelev table) where learning from multiple ....
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De Raedt, L., Blockeel, H., Dehaspe, L., and Van Laer, W. Three companions for data mining in first order logic. In (Dzeroski and Lavrac, 2001).
From Propositional to First Order Logic in Machine Learning and.. - Van Laer (2002) (1 citation) (Correct)
....in the next chapters. In the related work section, we discuss some other approaches to overcome the limitations of the propositional framework, such as transformation based methods or first order feature construction methods. Bibliographical note: This chapter is partly based on [Van Laer and De Raedt, 2001a; Van Laer and De Raedt, 200lb] and [Van Laer and De Raedt, 1998] 3.2 The propositional task and algorithm Suppose that we are asked to design a learning system for the Bongard like problems in Figure 1.1 and Figure 1.2. Machine learning researchers and practitioners would observe that these ....
....indeed overcomes the limitations of the propositional framework. Note that an example is labeled with a class c, where c is a ground atom fact (here class(positive) An example belongs to class c, if atom c is true in the example 2. 1This is consistent with the definition in [Van Laer and De taedt, 2001a] where an example is seen as a set of ground facts. 2This is similar to the extra column feature for class in a propositional representation. This first order representation of examples is more general and more expressive than the propositional representation, which is clearly a special case ....
[Article contains additional citation context not shown here]
L. De Raedt, H. Blockeel, L. Dehaspe, and W. Van Laer. Three companions for data mining in first order logic. In S. Dieroski and N. Lavra, editors, Relational Data Mining, pages 105-139. Springer-Verlag, 2001.
Decision Support Through Subgroup Discovery: - Three Case Studies (Correct)
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De Raedt, L., Blockeel, H., Dehaspe, L., & Laer, W. V. (2001). Three Companions for Data Mining in First Order Logic. In S. D zeroski & N. Lavra c (Eds.), Relational Data Mining. Springer-Verlag.
Local Patterns: Theory and Practice of Constraint-Based.. - Lavrac, Zelezny.. (Correct)
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Luc De Raedt, Hendrik Blockeel, Luc Dehaspe, and Wim Van Laer. Three companions for data mining in first order logic. In Saso Dzeroski and Nada Lavrac, editors, Relational Data Mining, pages 105--139. Springer-Verlag, 2001.
Multi-Relational Decision Tree Algorithm - Implementation and.. - Atramentov (2003) (Correct)
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De Raedt, L., Blockeel, H., Dehaspe, L., and Van Laer, W.: Three companions for data mining in first order logic. In: Dzeroski, S. and Lavrac, N. editors. Relational Data Mining. Springer-Verlag (2001)
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