In the context of frequent pattern discovery, we present a generality relation, called thetaOI-subsumption, which is based on the assumption of Object Identity in spaces of patterns to be intended as existentially quantified conjunctive formulae. The resulting generality order ¹OI seems appropriate for organizing efficiently the space of DATALOG patterns over structured domains. Indeed we prove the existence of ideal refinement operators for ¹OI-ordered spaces and the monotonicity of ¹OI with respect to pattern support. Features of such spaces are illustrated by means of an example of frequent pattern discovery in spatial data
Modern order and lattice theory provides convenient mathematical tools for pattern mining, in partic...
Structural matching, originally introduced by Steven Vere, implements and formalizes the notion of a...
In this paper, we analyze the main topological properties of a relevant class of topologies associat...
In the context of frequent pattern discovery, we present a generality relation, called thetaOI-subs...
A framework for theory refinement is presented pursuing the efficiency and effectiveness of learning...
We present a framework for theory refinement operators fulfilling some desirable properties in order...
We present a framework for theory refinement operators ful- filling properties that ensure the effic...
Refinement operators for theories avoid the problems related to the myopia of many relational learni...
Describing and capturing significant differences between two classes of data is an important data mi...
Weakening implication by assuming the object identity bias allows for both a model-theoretical and a...
The adoption of the object identity bias for weakening implication has lead to the definition of OI-...
In this paper, we show how the existence of taxonomies on objects and/or attributes can be used in f...
This chapter proposes that the stable coexistence of regular and irregular patterns can be understoo...
International audiencePattern mining consists in discovering interesting patterns in data. For that,...
We generalize the exact predictive regularity of symmetry groups to give an algebraic theory of patt...
Modern order and lattice theory provides convenient mathematical tools for pattern mining, in partic...
Structural matching, originally introduced by Steven Vere, implements and formalizes the notion of a...
In this paper, we analyze the main topological properties of a relevant class of topologies associat...
In the context of frequent pattern discovery, we present a generality relation, called thetaOI-subs...
A framework for theory refinement is presented pursuing the efficiency and effectiveness of learning...
We present a framework for theory refinement operators fulfilling some desirable properties in order...
We present a framework for theory refinement operators ful- filling properties that ensure the effic...
Refinement operators for theories avoid the problems related to the myopia of many relational learni...
Describing and capturing significant differences between two classes of data is an important data mi...
Weakening implication by assuming the object identity bias allows for both a model-theoretical and a...
The adoption of the object identity bias for weakening implication has lead to the definition of OI-...
In this paper, we show how the existence of taxonomies on objects and/or attributes can be used in f...
This chapter proposes that the stable coexistence of regular and irregular patterns can be understoo...
International audiencePattern mining consists in discovering interesting patterns in data. For that,...
We generalize the exact predictive regularity of symmetry groups to give an algebraic theory of patt...
Modern order and lattice theory provides convenient mathematical tools for pattern mining, in partic...
Structural matching, originally introduced by Steven Vere, implements and formalizes the notion of a...
In this paper, we analyze the main topological properties of a relevant class of topologies associat...