How to Upgrade Propositional Learners to First Order Logic: A Case Study

Laer, Wim Van; Raedt, Luc De

doi:10.1007/3-540-44673-7_5

Cited by 10 publications

(10 citation statements)

References 39 publications

(16 reference statements)

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“…A general methodology for upgrading a standard data mining algorithm to a relational case was proposed in Ref. . This methodology is set in an inductive logic programming (ILP) environment and includes the following steps: …”

Section: Related Workmentioning

confidence: 99%

“…On the basis of practice with upgrading propositional algorithms to a relational case, a general methodology for this task was developed in Ref. . In this methodology, the process of upgrading starts with choosing a propositional algorithm that best matches a given data mining task.…”

Section: Introductionmentioning

confidence: 99%

“…This is an upgrade of a granular computing based data mining framework to a relational case. The general outline of the process of upgrading is inspired by the methodology for extending attribute‐value algorithms (details are given in Section ). In the methodology proposed in this paper, we start with introducing a general granular computing based framework for mining data.…”

Section: Introductionmentioning

confidence: 99%

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Upgrading a Granular Computing Based Data Mining Framework to a Relational Case

Hońko

2014

Int. J. Intell. Syst.

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One of the popular methods to develop an algorithm for mining data stored in a relational structure is to upgrade an existing attribute-value algorithm to a relational case. Current approaches to this problem have some shortcomings such as (1) a dependence on the upgrading process of the algorithm to be extended, (2) complicated redefinitions of crucial notions (e.g., pattern generality, pattern refinement), and (3) a tolerant limitation of the search space for pattern discovery. In this paper, we propose and evaluate a general methodology for upgrading a data mining framework to a relational case. This methodology is defined in a granular computing environment. Thanks to our relational extension of a granular computing based data mining framework, the three above problems can be overcome. C 2014 Wiley Periodicals, Inc.In the following sections, we review other research works related to problem addressed in this paper (Section 2), introduce a granular computing based data mining framework (Section 3), and based on it, we develop (Section 4) and evaluate (Section 5), a general methodology for mining relational data, and finally provide concluding remarks (Section 6). RELATED WORKSMany of first algorithms for mining relational data were developed based on algorithms devoted to propositional data. The task of upgrading a standard data mining algorithm to a relational case is not trivial and requires much attention. An upgraded algorithm should preserve as many features of the original algorithm as possible. In other words, only crucial notions, for example, data and patterns representation, are upgraded. Furthermore, the original algorithm should be a special case of its relational counterpart, that is, they both should produce the same results for identical propositional data. Comparing the above-described information systems with the ones employed in this paper, one can state that a restricted compound information system, introduced in this paper, is most similar to the constrained sum of information systems. On one hand, our system can be treated as a special case of the other system because the constraint, which is generally defined in the latter, can be formed by using formulas that join tables. On the other hand, our system is constructed in a different way because the constraint is not imposed directly, that is, left outer joins on the formulas are used.One can conclude that the existing approaches propose useful information systems to deal with data stored in a relational structure; however, they do not study extensively or at all the problem of construction of information granules for relational data using an (extended) attribute-value language. GRANULAR COMPUTING BASED DATA MINING FRAMEWORKIn this section, we introduce a granular computing based data mining framework, 7 which is constructed based on definitions from Refs. 18, 20. SEM I S i∧j (α 1 ∧ α 2 ) = SEM I S i∧j (α 1 ) ∩ SEM I S i∧j (α 2 ); 4. α 1 , α 2 ∈ L I S i∧j ⇒ α 1 ∨ α 2 ∈ L I S i∧j , SEM I S i∧j (α 1 ∨ α 2 ) = SEM I S i∧j (α 1 ) ∪ SEM I S i∧j ...

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Upgrading a Granular Computing Based Data Mining Framework to a Relational Case

Hońko

2014

Int. J. Intell. Syst.

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“…Most relational data mining algorithms come from the field of inductive logic programming (ILP) (Lavrac et al 1994), and certain derivatives. ILP systems dealing with classification tasks typically adopt the covering approach of rule induction systems (Van Laer et al 2001). It is also possible in many ILP systems to enter known rules, which means you don't have to generate every rule but can rather define it yourself if some common knowledge exists already.…”

Section: Inductive Logic Programming and Related Conceptsmentioning

confidence: 99%

Leveraging Network Effects for Predictive Modelling in Customer Relationship Management

Kiss¹,

Bichler

2005

SSRN Journal

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“…Following the methodology reported in [40] for upgrading propositional learners towards multi-relational representations, we identify the propositional model tree learner SMOTI [29] as the best matching for the learning task. SMOTI integrates the partitioning and the prediction stages.…”

Section: Introductionmentioning

confidence: 99%

Multi-Relational Model Tree Induction Tightly-Coupled with a Relational Database

Appice

Ceci

Malerba

2014

Fundamenta Informaticae

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Multi-Relational Data Mining (MRDM) refers to the process of discovering implicit, previously unknown and potentially useful information from data scattered in multiple tables of a relational database. Following the mainstream of MRDM research, we tackle the regression where the goal is to examine samples of past experience with known continuous answers (response) and generalize future cases through an inductive process. Mr-SMOTI, the solution we propose, resorts to the structural approach in order to recursively partition data stored into a tightly-coupled database and build a multi-relational model tree which captures the linear dependence between the response variable and one or more explanatory variables. The model tree is top-down induced by choosing, at each step, either to partition the training space or to introduce a regression variable in the linear models with the leaves. The tight-coupling with the database makes the knowledge on data structures (foreign keys) available free of charge to guide the search in the multi-relational pattern space. Experiments on artificial and real databases demonstrate that in general Mr-SMOTI outperforms both SMOTI and M5' which are two propositional model tree induction systems, and TILDE-RT which is a state-of-art structural model tree induction system. A. Appice et al. / Multi-Relational Model Tree Induction Tightly-Coupled with a Relational DatabaseSome examples of applications are found in mining the web, which can be seen as a massive graph of linked pages [19], in social network analysis, which focuses on modeling the relationships between social (individuals, groups, organizations) and computational (computers and web sites) actors [38], in computational biology, which studies interactions between components (genes and proteins) of the living cells [33], and in mining geo-referenced objects, which are characterized by implicit spatial relationships defined by geometry and positioning [28].Multi-relational data mining (MRDM) is a multi-disciplinary field which provides sound methods for the discovery of relational patterns that involve multiple relations and are stated in a more expressive language (e.g., predicate calculus and SQL) than patterns defined on a single data table.Many data mining tasks (e.g., classification, clustering, association analysis) have been adapted to a multi-relational setting. In particular, several studies have been reported for the regression task, which is the main focus of this paper. In the regression task, some labeled observations (training cases) are given, which are instances of an unknown continuous function f , and the problem is to build a general model that approximates the function f and can be subsequently used to make predictions on new unlabeled observations.Handling multi-relational data adds difficulties to the regression task. First of all, data available in distinct database relations describe different objects (or entities) involved in the phenomenon under study. These objects play different roles, and it is neces...

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How to Upgrade Propositional Learners to First Order Logic: A Case Study

Cited by 10 publications

References 39 publications

Upgrading a Granular Computing Based Data Mining Framework to a Relational Case

Upgrading a Granular Computing Based Data Mining Framework to a Relational Case

Leveraging Network Effects for Predictive Modelling in Customer Relationship Management

Multi-Relational Model Tree Induction Tightly-Coupled with a Relational Database

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