Kramer, S., Raedt, L. D., Helma, C.: Molecular Feature Mining in HIV data, Int'l Conf. on Knowledge Discovery and Data Mining, 2001. Home/Search Document Not in Database Summary Related Articles Check |
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Constraint Based Mining of First Order Sequences in SeqLog.. - Lee, De Raedt (Correct)
....expressive framework given that one can compose complex anti monotonic (resp. monotonic) constraints on the basis of simpler ones. Within MineSeqLog, the following primitives are directly supported. They are inspired on similar primitives for simple sequences in our molecular feature miner MolFea [10]. T p, p T , T # p) and (p # T ) where T is the unknown target query and p is a logical sequence; this type of primitive constraint denotes that T should (resp. should not) subsume the sequence p; e.g. the constraint a b c T specifies that the target pattern T should be subsumed ....
....anti monotonic queries can be represented using the set of minimally general elements. Furthermore, various algorithms exploit these properties for e#ciently finding solutions to queries, cf. e.g. Bayardo s MaxMiner and Mitchell s candidate elimination algorithm [16] as well as our MolFea system [10]. The boundary sets, sometimes also called the borders, are the most specific (resp. the most general) patterns within (or just outside) the set. More formally, let P be a set of patterns. Then we denote the set of minimal (i.e. minimally general) patterns within P as min(P ) In Mannila and ....
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Stefan Kramer, Luc De Raedt, and Christoph Helma. Molecular feature mining in hiv data. In KDD-2001: The Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, August 26--29 2001. ISBN: 158113391X.
Frequent Sub-Structure-Based Approaches for.. - Deshpande, Kuramochi, .. (2003) (4 citations) (Correct)
....it not only represents the atoms but also represents the bonds between different atoms. The SMILES representation for Flucytosine is Nc1nc(O)ncc1F. Though SMILES representation is a compact it is not guaranteed to be unique, furthermore the representation is quite restrictive to work with [22]. The activity of a compound largely depends on its chemical structure and the arrangement of different atoms in 3D space. As a result, effective classification algorithms must be able to directly take into account the structural nature of these datasets. In this paper we represent each compound ....
....also later extended to classify graphs and was referred as SubdueCL [14] In SubdueCL instead of using minimum description length as a heuristic a measure similar to confidence of a subgraph is used as a heuristic. Finally, another heuristics based scheme targeted for chemical compounds is MOLFEA [22]. In this scheme each chemical compound is represented as a SMILES string, and is thought of as sequence of SMILES objects. This representation simplifies the problem to discovering frequently occurring sub sequences. 4 Classification Based on Frequent Subgraphs The previous research on ....
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S. Kramer, L. De Raedt, and C. Helma. Molecular feature mining in hiv data. In 7th International Conference on Knowledge Discovery and Data Mining, 2001.
Version Spaces in Constraint-Based Data Mining - Fischer (2003) Self-citation (Kramer De raedt) (Correct)
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S. Kramer, L. De Raedt, and C. Helma. Molecular feature mining in HIV data. In F. Provost and R. Srikant, editors, Proc. of the 7th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pages 136143. ACM Press, 2001.
Towards Optimizing Conjunctive Inductive Queries - Fischer, De Raedt Self-citation (De raedt) (Correct)
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S. Kramer, L. De Raedt, C. Helma. Molecular Feature Mining in HIV Data. In Proc. SIGKDD, 2001.
Frequent Free Tree Discovery in Graph Data - Rückert, Kramer (2003) (2 citations) Self-citation (Kramer) (Correct)
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S. Kramer, L. De Raedt, and C. Helma. Molecular feature mining in HIV data. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-01), pages 136--143, 2001.
Frequent Free Tree Discovery in Graph Data - Rückert, Kramer (2003) (2 citations) Self-citation (Kramer) (Correct)
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S. Kramer, L. D. Raedt, and C. Helma. Molecular feature mining in HIV data. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-01), pages 136--143, 2001.
Demand-Driven Construction of Structural Features in ILP - Kramer (2001) (6 citations) Self-citation (Kramer) (Correct)
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S. Kramer, L. De Raedt, C. Helma. Molecular Feature Mining in HIV Data. in: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-01), 2001.
A Theory of Inductive Query Answering - De Raedt, Jaeger, Lee, Mannila (2002) (4 citations) Self-citation (De raedt) (Correct)
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S. Kramer, L. De Raedt, C. Helma. Molecular Feature Mining in HIV Data. In Proc. SIGKDD, 2001.
A Theory of Inductive Query Answering - De Raedt, Jaeger, Lee, Mannila (2002) (4 citations) Self-citation (De raedt) (Correct)
....paper. More specifically, we study inductive queries that are boolean expressions over monotonic and anti monotonic predicates. An example query could ask for molecular fragments that have frequency at least 30 per cent in the active molecules or frequency at most 5 per cent in the inactive ones [14]. To the best of our knowledge this type of boolean inductive query is the most general type of inductive query that has been considered so far in the data mining literature. Indeed, most contemporary approaches to constraint based data mining use either single constraints (such as minimum ....
....so far in the data mining literature. Indeed, most contemporary approaches to constraint based data mining use either single constraints (such as minimum frequency) e.g. 2] a conjunction of monotonic constraints, e.g. 17, 10] or a conjunction of monotonic and anti monotonic constraints, e.g. [4, 14]. However, 6] has studied a specific type of boolean constraints in the context of association rules and item sets. It should also be noted that even these simpler types of queries have proven to be useful across several applications, which in turn explains the popularity of constraint based ....
[Article contains additional citation context not shown here]
S. Kramer, L. De Raedt, C. Helma. Molecular Feature Mining in HIV Data. In Proc. SIGKDD, 2001.
Efficiently Mining Frequent Embedded Unordered Trees - Zaki (2005) (Correct)
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Kramer, S., Raedt, L. D., Helma, C.: Molecular Feature Mining in HIV data, Int'l Conf. on Knowledge Discovery and Data Mining, 2001.
A Comparison of Approaches for Learning Probability Trees - Fierens, Ramon.. (2005) (Correct)
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S. Kramer, L. De Raedt, and C. Helma. Molecular feature mining in HIV data. In Proceedings of the 7th ACM SIGKDD International Conference on Know l edge Discovery and Data Mining (KDD01), pages 136--143, 2001.
Weighted Decomposition Kernels - Menchetti, Costa, Frasconi (2005) (Correct)
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Kramer, S., Raedt, L. D., & Helma, C. (2001). Molecular feature mining in HIV data. Proc. of KDD-01 (pp. 136--143). ACM Press.
TREE² - Decision Trees for Tree Structured Data - Bringmann, Zimmermann (2005) (Correct)
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Stefan Kramer, Luc De Raedt, and Christoph Helma. Molecular feature mining in HIV data. In Foster Provost and Ramakrishnan Srikant, editors, Proc. KDD-01, pages 136--143, New York, August 26--29 2001. ACM Press.
A Comparison of Approaches for Learning.. - Fierens, Ramon.. (2005) (Correct)
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S. Kramer, L. De Raedt, and C. Helma. Molecular feature mining in HIV data. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-01), pages 136--143. ACM Press, 2001.
A Systematic Approach for Optimizing Complex Mining Tasks on.. - Jin, Agrawal (Correct)
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Stefan Kramer, Luc De Raedt, and Christoph Helma. Molecular feature mining in hiv data. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pages 136--143, 2001.
Automated Approaches for Classifying Structures - Mukund Deshpande Deshpand (2002) (Correct)
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S. Kramer, L. De Raedt, and C. Helma. Molecular feature mining in hiv data. In ### ############# ########## ## ######### ######### ### #### ######, 2001.
Finding Frequent Patterns in a Large Sparse Graph - Kuramochi, Karypis (2004) (2 citations) (Correct)
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S. Kramer, L. De Raedt, and C. Helma. Molecular feature mining in HIV data. In Proc. of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-01), pages 136--143, 2001.
Finding Frequent Patterns in a Large Sparse Graph - Kuramochi, Karypis (2003) (2 citations) (Correct)
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S. Kramer, L. De Raedt, and C. Helma. Molecular feature mining in HIV data. In Proc. of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-01), pages 136--143, 2001.
Scalability and Efficiency in Multi-Relational Data Mining - Blockeel, Sebag (2003) (1 citation) (Correct)
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S. Kramer, L. De Raedt, and C. Helma. Molecular feature mining in HIV data. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-01), pages 136-143. ACM Press, 2001.
Constraint Based Mining of First Order - Sequences In Seqlog (Correct)
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Stefan Kramer, Luc De Raedt, and Christoph Helma. Molecular feature mining in hiv data. In KDD-2001.
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