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A Perspective on Inductive Logic Programming
"... . The stateoftheart in inductive logic programming is surveyed by analyzing the approach taken by this field over the past 8 years. The analysis investigates the roles of 1) logic programming and machine learning, of 2) theory, techniques and applications, of 3) various technical problems address ..."
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Cited by 55 (8 self)
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. The stateoftheart in inductive logic programming is surveyed by analyzing the approach taken by this field over the past 8 years. The analysis investigates the roles of 1) logic programming and machine learning, of 2) theory, techniques and applications, of 3) various technical problems addressed within inductive logic programming. 1 Introduction The term inductive logic programming was first coined by Stephen Muggleton in 1990 [1]. Inductive logic programming is concerned with the study of inductive machine learning within the representations offered by computational logic. Since 1991, annual international workshops have been organized [28]. This paper is an attempt to analyze the developments within this field. Particular attention is devoted to the relation between inductive logic programming and its neighboring fields such as machine learning, computational logic and data mining, and to the role that theory, techniques and implementations, and applications play. The analysis...
Subgroup Discovery with CN2SD
 Journal of Machine Learning Research
, 2004
"... discovery. The goal of subgroup discovery is to find rules describing subsets of the population that are sufficiently large and statistically unusual. The paper presents a subgroup discovery algorithm, CN2SD, developed by modifying parts of the CN2 classification rule learner: its covering algorit ..."
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Cited by 52 (10 self)
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discovery. The goal of subgroup discovery is to find rules describing subsets of the population that are sufficiently large and statistically unusual. The paper presents a subgroup discovery algorithm, CN2SD, developed by modifying parts of the CN2 classification rule learner: its covering algorithm, search heuristic, probabilistic classification of instances, and evaluation measures. Experimental evaluation of CN2SD on 23 UCI data sets shows substantial reduction of the number of induced rules, increased rule coverage and rule significance, as well as slight improvements in terms of the area under ROC curve, when compared with the CN2 algorithm. Application of CN2SD to a large traffic accident data set confirms these findings.
Comparative Evaluation of Approaches to Propositionalization
, 2003
"... Propositionalization has already been shown to be a promising approach for robustly and e#ectively handling relational data sets for knowledge discovery. In this paper, we compare uptodate methods for propositionalization from two main groups: logicoriented and databaseoriented techniques. Ex ..."
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Cited by 38 (2 self)
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Propositionalization has already been shown to be a promising approach for robustly and e#ectively handling relational data sets for knowledge discovery. In this paper, we compare uptodate methods for propositionalization from two main groups: logicoriented and databaseoriented techniques. Experiments using several learning tasks  both ILP benchmarks and tasks from recent international data mining competitions  show that both groups have their specific advantages. While logicoriented methods can handle complex background knowledge and provide expressive firstorder models, databaseoriented methods can be more e#cient especially on larger data sets. Obtained accuracies vary such that a combination of the features produced by both groups seems a further valuable venture.
Discovery of Relational Association Rules
 Relational data mining
, 2000
"... Within KDD, the discovery of frequent patterns has been studied in a variety of settings. In its simplest form, known from association rule mining, the task is to discover all frequent item sets, i.e., all combinations of items that are found in a sufficient number of examples. ..."
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Cited by 34 (1 self)
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Within KDD, the discovery of frequent patterns has been studied in a variety of settings. In its simplest form, known from association rule mining, the task is to discover all frequent item sets, i.e., all combinations of items that are found in a sufficient number of examples.
ExpertGuided Subgroup Discovery: Methodology and Application
 Journal of Artificial Intelligence Research
, 2002
"... This paper presents an approach to expertguided subgroup discovery. The main step of the subgroup discovery process, the induction of subgroup descriptions, is performed by a heuristic beam search algorithm, using a novel parametrized definition of rule quality which is analyzed in detail. The othe ..."
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Cited by 32 (7 self)
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This paper presents an approach to expertguided subgroup discovery. The main step of the subgroup discovery process, the induction of subgroup descriptions, is performed by a heuristic beam search algorithm, using a novel parametrized definition of rule quality which is analyzed in detail. The other important steps of the proposed subgroup discovery process are the detection of statistically significant properties of selected subgroups and subgroup visualization: statistically significant properties are used to enrich the descriptions of induced subgroups, while the visualization shows subgroup properties in the form of distributions of the numbers of examples in the subgroups. The approach is illustrated by the results obtained for a medical problem of early detection of patient risk groups.
Exploiting Background Knowledge for KnowledgeIntensive Subgroup Discovery
 In: Proc. 19th Intl. Joint Conference on Artificial Intelligence (IJCAI05
, 2005
"... In general, knowledgeintensive data mining methods exploit background knowledge to improve the quality of their results. Then, in knowledgerich domains often the interestingness of the mined patterns can be increased significantly. In this paper we categorize several classes of background knowledg ..."
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Cited by 31 (21 self)
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In general, knowledgeintensive data mining methods exploit background knowledge to improve the quality of their results. Then, in knowledgerich domains often the interestingness of the mined patterns can be increased significantly. In this paper we categorize several classes of background knowledge for subgroup discovery, and present how the necessary knowledge elements can be modelled. Furthermore, we show how subgroup discovery methods benefit from the utilization of background knowledge, and discuss its application in an incremental processmodel. The context of our work is to identify interesting diagnostic patterns to supplement a medical documentation and consultation system. We provide a case study in the medical domain, using a case base from a realworld application. 1
Confirmationguided discovery of firstorder rules with Tertius
 Machine Learning
, 2000
"... . This paper deals with learning firstorder logic rules from data lacking an explicit classification predicate. Consequently, the learned rules are not restricted to predicate definitions as in supervised inductive logic programming. Firstorder logic offers the ability to deal with structured, mul ..."
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Cited by 29 (9 self)
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. This paper deals with learning firstorder logic rules from data lacking an explicit classification predicate. Consequently, the learned rules are not restricted to predicate definitions as in supervised inductive logic programming. Firstorder logic offers the ability to deal with structured, multirelational knowledge. Possible applications include firstorder knowledge discovery, induction of integrity constraints in databases, multiple predicate learning, and learning mixed theories of predicate definitions and integrity constraints. One of the contributions of our work is a heuristic measure of confirmation, trading off novelty and satisfaction of the rule. The approach has been implemented in the Tertius system. The system performs an optimal bestfirst search, finding the k most confirmed hypotheses, and includes a nonredundant refinement operator to avoid duplicates in the search. Tertius can be adapted to many different domains by tuning its parameters, and it can deal eithe...
Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining
 Journal of Machine Learning Research
"... This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and subgroup discovery (SD) in a unifying framework named supervised descriptive rule discovery. While all these research areas aim at discovering patterns in the form of rules induced from labeled data, they use ..."
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Cited by 25 (0 self)
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This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and subgroup discovery (SD) in a unifying framework named supervised descriptive rule discovery. While all these research areas aim at discovering patterns in the form of rules induced from labeled data, they use different terminology and task definitions, claim to have different goals, claim to use different rule learning heuristics, and use different means for selecting subsets of induced patterns. This paper contributes a novel understanding of these subareas of data mining by presenting a unified terminology, by explaining the apparent differences between the learning tasks as variants of a unique supervised descriptive rule discovery task and by exploring the apparent differences between the approaches. It also shows that various rule learning heuristics used in CSM, EPM and SD algorithms all aim at optimizing a trade off between rule coverage and precision. The commonalities (and differences) between the approaches are showcased on a selection of best known variants of CSM, EPM and SD algorithms. The paper also provides a critical survey of existing supervised descriptive rule discovery visualization methods.
RSD: Relational subgroup discovery through firstorder feature construction
 In 12th International Conference on Inductive Logic Programming
, 2002
"... Relational rule learning is typically used in solving classification and prediction tasks. However, relational rule learning can be adapted also to subgroup discovery. This paper proposes a propositionalization approach to relational subgroup discovery, achieved through appropriately adapting rule l ..."
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Cited by 24 (7 self)
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Relational rule learning is typically used in solving classification and prediction tasks. However, relational rule learning can be adapted also to subgroup discovery. This paper proposes a propositionalization approach to relational subgroup discovery, achieved through appropriately adapting rule learning and firstorder feature construction.
Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling
 Journal of Machine Learning Research
, 2001
"... Many discovery problems, e.g., subgroup or association rule discovery, can naturally be cast as nbest hypotheses problems where the goal is to nd the n hypotheses from a given hypothesis space that score best according to a certain utility function. We present a sampling algorithm that solves this ..."
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Cited by 24 (4 self)
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Many discovery problems, e.g., subgroup or association rule discovery, can naturally be cast as nbest hypotheses problems where the goal is to nd the n hypotheses from a given hypothesis space that score best according to a certain utility function. We present a sampling algorithm that solves this problem by issuing a small number of database queries while guaranteeing precise bounds on con dence and quality of solutions. Known sampling approaches have treated single hypothesis selection problems, assuming that the utility be the average (over the examples) of some function  which is not the case for many frequently used utility functions. We show that our algorithm works for all utilities that can be estimated with bounded error. We provide these error bounds and resulting worstcase sample bounds for some of the most frequently used utilities, and prove that there is no sampling algorithm for a popular class of utility functions that cannot be estimated with bounded error. The algorithm is sequential in the sense that it starts to return (or discard) hypotheses that already seem to be particularly good (or bad) after a few examples. Thus, the algorithm is almost always faster than its worstcase bounds.