We study how probabilistic reasoning and inductive querying can be combined within ProbLog, a recent probabilistic extension of Prolog. ProbLog can be regarded as a database system that supports both probabilistic and inductive reasoning through a variety of querying mechanisms. After a short introduction to ProbLog, we provide a survey of the different types of inductive queries that ProbLog supports, and show how it can be applied to the mining of large biological networks.Peer reviewe

De Raedt, Luc

Gutmann, Bernd

Kersting, Kristian

Kimmig, Angelika

Santos Costa, Vitor

Toivonen, Hannu

Helsingin yliopiston digitaalinen arkisto

Probabilistic Inductive Querying Using ProbLog

This book is about inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The aim of the book as to provide an overview of the state-of- the art in this novel and - citing research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the uni?cation of pattern mining approaches through constraint programming, the clari?cation of the re- tionship between mining local patterns and global models, and the proposed in- grative frameworks and approaches for inducive databases. On the application side, applications to practically relevant problems from bioinformatics are presented. Inductive databases (IDBs) represent a database view on data mining and kno- edge discovery. IDBs contain not only data, but also generalizations (patterns and models) valid in the data. In an IDB, ordinary queries can be used to access and - nipulate data, while inductive queries can be used to generate (mine), manipulate, and apply patterns and models. In the IDB framework, patterns and models become ”?rst-class citizens” and KDD becomes an extended querying process in which both the data and the patterns/models that hold in the data are queried

Besson, Jérémy

Boulicaut, Jean-François

Guns, Tias

Nijssen, Siegfried

DIAL UCLouvain

Generalizing itemset mining in a constraint programming setting

We study how probabilistic reasoning and inductive querying can be combined within ProbLog, a recent probabilistic extension of Prolog. ProbLog can be regarded as a database system that supports both probabilistic and inductive reasoning through a variety of querying mechanisms. After a short introduction to ProbLog, we provide a survey of the different types of inductive queries that ProbLog supports, and show how it can be applied to the mining of large biological networks

De Raedt, L.

Kimmig, A.

Gutmann, B.

Kersting, K.

Costa, V.S.

Toivonen, H.

Fraunhofer-ePrints

Probabilistic inductive querying using problog

Björn Bringmann

Mukund Deshpande

Jeroen Kazius

T.M. Mitchell

G. Nemhauser

Geoffrey I. Webb

Crossref

From Local Patterns to Classification Models

International audienceIn recent years, a large number of algorithms have been proposed forfinding set patterns in binary data. This includes popular miningtasks based on, for instance, frequent (closed) itemsets.  In thischapter, we develop a common framework in which these algorithms canbe studied thanks to the principles of constraint programming. Weshow how such principles can be applied both in specialized andgeneral solvers

Hal-Diderot

Generalizing Itemset Mining in a Constraint Programming Setting

Using pattern mining techniques for building a predictive model is currently a popular topic of research. The aim of these techniques is to obtain classifiers of better predictive performance as compared to greedily constructed models, as well as to allow the construction of predictive models for data not represented in attribute-value vectors. In this chapter we provide an overview of recent techniques we developed for integrating pattern mining and classification tasks. The range of techniques spans the entire range from approaches that select relevant patterns from a previously mined set for propositionalization of the data, over inducing patternbased rule sets, to algorithms that integrate pattern mining and model construction. We provide an overview of the algorithms which are most closely related to our approaches in order to put our techniques in a context

Bringmann, Björn

Zimmermann, Albrecht

From local patterns to classification models

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.310.952

Probabilistic Inductive Querying Using ProbLog

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Helsingin yliopiston digitaalinen arkisto

DIAL UCLouvain

Fraunhofer-ePrints

Crossref

Hal-Diderot

DIAL UCLouvain