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Discovery of frequent DATALOG patterns
TLDR
WARMR is presented, a general purpose inductive logic programming algorithm that addresses frequent query discovery: a very general DATALOG formulation of the frequent pattern discovery problem.
Mining Association Rules in Multiple Relations
TLDR
The system Warmr is presented, which extends Apriori to mine association rules in multiple relations, and is applied to the natural language processing task of mining part-of-speech tagging rules in a large corpus of English.
Discovery of relational association rules
TLDR
Algorithms for relational association rule discovery that are well-suited for exploratory data mining are presented, which offer the flexibility required to experiment with examples more complex than feature vectors and patternsMore complex than item sets.
Clausal Discovery
TLDR
CLAUDIEN is an inductive logic programming engine that fits in the descriptive data mining paradigm and employs a novel declarative bias mechanism to define the set of clauses that may appear in a hypothesis.
Finding Frequent Substructures in Chemical Compounds
TLDR
This paper applies data mining to the problem of predicting chemical carcinogenicity, and presents a knowledge discovery method for structured data, where patterns reflect the one- to-many and many-to-many relationships of several tables.
Improving the Efficiency of Inductive Logic Programming Through the Use of Query Packs
TLDR
A complexity analysis shows that considerable efficiency improvements can be achieved through the use of this query pack execution mechanism, and this claim is supported by empirical results obtained by incorporating support for queryPack execution in two existing learning systems.
Frequent Pattern Discovery in First-Order Logic
TLDR
A general formulation of the frequent pattern discovery problem, where both the database and the patterns are represented in some subset of first-order logic, i.e., essentially Prolog, and each example as it were corresponds to a (mini-)database.
Maximum Entropy Modeling with Clausal Constraints
TLDR
First experiments indicate MACCENT may be useful for prediction, and for classification in cases where the induced model should be combined with other stochastic information sources, and on an existing maximum-likelihood approach to maximum entropy modeling.
Executing Query Packs in ILP
TLDR
This work has incorporated their query pack execution mechanism in the ILP systems TILDE and WARMR by implementing a new Prolog engine ILPROLOG which provides support for pack execution at a lower level, and demonstrates significant efficiency gains.
Warmr: a data mining tool for chemical data
TLDR
Warmr is presented, the first ILP data mining algorithm to be applied to chemoinformatic data, and the substructures were used to prove that there existed no accurate rule, based purely on atom-bond substructure with less than seven conditions, that could predict carcinogenicity.
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