From Temporal Rules to Temporal Meta-rules

Cotofrei, Paul; Stoffel, Kilian

doi:10.1007/978-3-540-30076-2_17

Cited by 13 publications

(9 citation statements)

References 6 publications

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“…However, the meta-rules obtained have the traditional formulation (A → B) but with a small variability of the estimated confidence among different pre-established time structures. Needless to say, our concept of meta-rule is different to those proposed in these works [18,3,11], because we may have association rules in the antecedent and/or in the consequent of the meta-rule and its meaning does not correspond to the same concepts.…”

Section: Related Workmentioning

confidence: 97%

“…In higher order mining, we have found two different approaches using also the concept of meta-rule. In [11], the authors called temporal meta-rules to those rules found from temporal rules (firstorder rules) by a second inference process. However, the meta-rules obtained have the traditional formulation (A → B) but with a small variability of the estimated confidence among different pre-established time structures.…”

Section: Related Workmentioning

confidence: 99%

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Meta-association rules for mining interesting associations in multiple datasets

Ruiz

Gmez-Romero

Molina-Solana

et al. 2016

Applied Soft Computing

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Association rules have been widely used in many application areas to extract new and useful information expressed in a comprehensive way for decision makers from raw data. However, raw data may not always be available, it can be distributed in multiple datasets and therefore there resulting number of association rules to be inspected is overwhelming. In the light of these observations, we propose meta-association rules, a new framework for mining association rules over previously discovered rules in multiple databases. Meta-association rules are a new tool that convey new information from the patterns extracted from multiple datasets and give a "summarized" representation about most frequent patterns. We propose and compare two different algorithms based respectively on crisp rules and fuzzy rules, concluding that fuzzy meta-association rules are suitable to incorporate to the meta-mining procedure the obtained quality assessment provided by the rules in the first step of the process, although it consumes more time than the crisp approach. In addition, fuzzy meta-rules give a more manageable set of rules for its posterior analysis and they allow the use of fuzzy items to express additional knowledge about the original databases. The proposed framework is illustrated with real-life data about crime incidents in the city of Chicago. Issues such as the difference with traditional approaches are discussed using synthetic data.

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Section: Related Workmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Meta-association rules for mining interesting associations in multiple datasets

Ruiz

Gmez-Romero

Molina-Solana

et al. 2016

Applied Soft Computing

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“…The temporal ontology, on which the logical formalism introduced in Cotofrei & Stoffel (2004;2005) was constructed, is represented by linearly ordered discrete instants. Syntactically, all the terms and formulae are defined over a restricted first-order temporal language L containing constant symbols, n-ary function symbols, variable symbols {y 1 , y 2 , ...}, n-ary predicate symbols (n ≥ 1), the set of relational symbols {=, <, ≤, >, ≥}, the logical connective ∧ and a temporal connective of the form ∇ k , k ∈ Z,wherek strictly positive means after k time instants, k strictly negative means before k time instant and k = 0meansnow.…”

Section: Logical Formalism Of Temporal Rulesmentioning

confidence: 99%

Temporal Rules over Time Structures with Different Granularities - a Stochastic Approach

Cotofrei¹,

Stoffel²

2011

New Fundamental Technologies in Data Mining

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“…In [1,5,21] general frameworks for temporal mining are proposed, but usually the researches on causal and temporal rules are more concentrated on the methodological/algorithmic aspect, and less on the theoretical aspect. Based on a methodology for temporal rule extraction, described in [9], we proposed in [10,11] an innovative formalism based on first-order temporal logic, which permits an abstract view on temporal rules. An important concept defined in this formalism is the property of consistency, which guarantees the preserving over time of the confidence/support of a temporal rule.…”

Section: Introductionmentioning

confidence: 99%

Time Granularity in Temporal Data Mining

Cotofrei

Stoffel

2009

Studies in Computational Intelligence

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Summary. 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.

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From Temporal Rules to Temporal Meta-rules

Cited by 13 publications

References 6 publications

Meta-association rules for mining interesting associations in multiple datasets

Meta-association rules for mining interesting associations in multiple datasets

Temporal Rules over Time Structures with Different Granularities - a Stochastic Approach

Time Granularity in Temporal Data Mining

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