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Cotofrei, Paul
Résultat de la recherche
Personalized view of Swiss Public Health Statistical Data
2018, De Santo, Alessio, Cotofrei, Paul, Stoffel, Kilian
Temporal Rules over Time Structures with Different Granularities - a Stochastic Approach
2011, Cotofrei, Paul, Stoffel, Kilian
Stochastic Processes and Temporal Rules
2006-5, Cotofrei, Paul, Stoffel, Kilian
From temporal rules to temporal meta-rules
2004-9, Cotofrei, Paul, Stoffel, Kilian
A Parallel Approach for Decision Trees Learning from Big Data Streams
2015-6-24, Calistru, Ionel Tudor, Cotofrei, Paul, Stoffel, Kilian
Time Granularity in Temporal Data Mining
2009, Cotofrei, Paul
In this chapter, a formalism for a specific temporal data mining task (the discovery of rules, inferred from databases of events having a temporal dimension), is defined. The proposed theoretical framework, based on first-order temporal logic, allows the definition of the main notions (event, temporal rule, confidence) in a formal way. This formalism is then extended to include the notion of temporal granularity and a detailed study is made to investigate the formal relationships between the support measures of the same event in linear time structures with different granularities. Finally, based on the concept of consistency, a strong result concerning the independence of the confidence measure for a temporal rule, over the worlds with different granularities, is proved.
Temporal granular logic for temporal data mining
2005-7, Cotofrei, Paul, Stoffel, Kilian
In this article, a formalism for a specific temporal data mining task (the discovery of rules, inferred from databases of events having a temporal dimension), is defined. The proposed theoretical framework, based on first-order temporal logic, allows the definition of the main notions (event, temporal rule, constraint) in a formal way. This formalism is then extended to include the notion of temporal granularity and a detailed study is made to investigate the formal relationships between semantics for the same event in linear time structures with different granularities.
A Clustering Topology forWireless Sensor Networks: New Semantics over Network Topology
2013-7-29, Calistru, Ionel Tudor, Cotofrei, Paul, Stoffel, Kilian
Stochastic Processes and Temporal Data Mining
2007-8, Cotofrei, Paul, Stoffel, Kilian
This article tries to give an answer to a fundamental question in temporal data mining: ”Under what conditions a temporal rule extracted from up-to-date temporal data keeps its confidence/support for future data”. A possible solution is given by using, on the one hand, a temporal logic formalism which allows the definition of the main notions (event, temporal rule, support, confidence) in a formal way and, on the other hand, the stochastic limit theory. Under this probabilistic temporal framework, the equivalence between the existence of the support of a temporal rule and the law of large numbers is systematically analysed.
First-Order Logic Based Formalism for Temporal Data Mining
2005, Cotofrei, Paul, Stoffel, Kilian
In this article we define a formalism for a methodology that has as purpose the discovery of knowledge, represented in the form of general Horn clauses, inferred from databases with a temporal dimension. To obtain what we called temporal rules, a discretisation phase that extracts events from raw data is applied first, followed by an induction phase, which constructs classification trees from these events. The theoretical framework we proposed, based on first-order temporal logic, permits us to define the main notions (event, temporal rule, constraint) in a formal way. The concept of consistent linear time structure allows us to introduce the notions of general interpretation and of confidence. These notions open the possibility to use statistical approaches in the design of algorithms for inferring higher order temporal rules, denoted temporal meta-rules.