By Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi
A profitable integration of constraint programming and information mining has the aptitude to steer to a brand new ICT paradigm with some distance attaining implications. it could actually swap the face of knowledge mining and computing device studying, in addition to constraint programming expertise. it can not just permit one to exploit info mining strategies in constraint programming to spot and replace constraints and optimization standards, but additionally to hire constraints and standards in facts mining and laptop studying as a way to observe versions suitable with previous wisdom.
This e-book reviews on a few key effects received in this built-in and move- disciplinary technique in the ecu FP7 FET Open venture no. 284715 on “Inductive Constraint Programming” and a few linked workshops and Dagstuhl seminars. The publication is established in 5 components: history; studying to version; studying to resolve; constraint programming for facts mining; and showcases.
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A winning integration of constraint programming and knowledge mining has the aptitude to steer to a brand new ICT paradigm with a long way attaining implications. it might probably switch the face of information mining and desktop studying, in addition to constraint programming expertise. it'll not just enable one to take advantage of information mining suggestions in constraint programming to spot and replace constraints and optimization standards, but additionally to hire constraints and standards in information mining and computer studying with the intention to become aware of versions appropriate with previous wisdom.
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Additional info for Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach
This chapter shows why the use of constraints is becoming an important and challenging task for the data mining community, since it requires a radical re-design of existing approaches in order to deﬁne and satisfy constraints during the whole knowledge extraction process. Table 1. Main characteristics of the diﬀerent classes of constraints Classiﬁcation Clustering Pattern Data phase: pre, mining type: hard phase: mining type: hard, soft phase: pre, mining type: hard Model phase: mining, post type: soft phase: mining type: soft, hard phase: mining type: hard, soft phase: mining type: hard phase: mining, post type: hard Measure phase: mining, post type: hard, soft Data Mining and Constraints: An Overview 41 Even though one of the aims of this chapter is to provide an introduction on the basic mining models and algorithms, it is worth stating that the basic concepts introduced along this overview are still valid also for advanced data mining analysis.
A popular approach of Bayesian classiﬁcation is na¨ıve Bayes. This kind of classiﬁer estimates the class-conditional probability, by assuming that the attributes are conditionally independent. To classify a record, the algorithm computes the posterior probability of a class value using Bayes theorem, and returns the class that maximizes this probability value. The way of computing class-conditional distribution varies in the presence of categorical or continuous attributes. In the ﬁrst case, the conditional probability is estimated using the fraction of training samples with a speciﬁc class label considering an attribute value.
Generally, the use of constraints does not necessarily guarantee the achievement of a solution. In order to control this eﬀect it can be necessary to relax constraints. This leads to the need of oﬀering the possibility of classifying constraints as either hard or soft, that is relaxable: • Hard constraint: a constraint is called hard if a model that violates it is unacceptable. The use of only this class of constraints can involve the discovery of empty solutions. A hard-constrained algorithm halts when there does not exist a state that satisﬁes all the constraints, and it returns no results [OY12].