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4,277
Dynamic Itemset Counting and Implication Rules for Market Basket Data
, 1997
"... We consider the problem of analyzing market-basket data and present several important contributions. First, we present a new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling. We in ..."
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Cited by 615 (6 self)
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investigate the idea of item reordering, which can improve the low-level efficiency of the algorithm. Second, we present a new way of generating "implication rules," which are normalized based on both the antecedent and the consequent and are truly implications (not simply a measure of co
Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory
- Journal of Economics
, 2000
"... We estimate a forward-looking monetary policy reaction function for the postwar United States economy, before and after Volcker’s appointment as Fed Chairman in 1979. Our results point to substantial differences in the estimated rule across periods. In particular, interest rate policy in the Volcker ..."
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Cited by 1266 (17 self)
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in the Volcker-Greenspan period appears to have been much more sensitive to changes in expected in�ation than in the pre-Volcker period. We then compare some of the implications of the estimated rules for the equilibrium properties of in�ation and output, using a simple macroeconomic model, and show
Conditionally Firing Implicative Rules
"... Conditionally firing rules have been proposed by B. Moser and M. Navara in order to preserve nat-ural properties that are not preserved by the usual setting of fuzzy inference systems such as Mamdani-Assilian rules joined to the CRI inference mecha-nism. In this paper, we follow this direction and s ..."
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and show, that if the axioms are naturally modified in order to capture the same semantic for the implica-tive rules, we can also define conditionally firing im-plicative rules that jointly with the Bandler-Kohout subproduct preserve exactly the same properties.
New Algorithms for Fast Discovery of Association Rules
- In 3rd Intl. Conf. on Knowledge Discovery and Data Mining
, 1997
"... Association rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent itemsets, and then forming conditional implication rules among them. In this paper we present efficient algorithms for the discovery ..."
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Cited by 397 (26 self)
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Association rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent itemsets, and then forming conditional implication rules among them. In this paper we present efficient algorithms
Semantics of Context-Free Languages
- In Mathematical Systems Theory
, 1968
"... "Meaning " may be assigned to a string in a context-free language by defining "at-tributes " of the symbols in a derivation tree for that string. The attributes can be de-fined by functions associated with each production in the grammar. This paper examines the implications of th ..."
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Cited by 569 (0 self)
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"Meaning " may be assigned to a string in a context-free language by defining "at-tributes " of the symbols in a derivation tree for that string. The attributes can be de-fined by functions associated with each production in the grammar. This paper examines the implications
Modelling Uncertain Implication Rules in Evidence Theory
"... Abstract — Often knowledge from human experts is expressed in the form of uncertain implication rules, such as “if A then B” with a certain degree of confidence. Therefore, it is important to include such a representation of knowledge into the process of reasoning under uncertainty. Incorporating in ..."
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Cited by 1 (0 self)
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Abstract — Often knowledge from human experts is expressed in the form of uncertain implication rules, such as “if A then B” with a certain degree of confidence. Therefore, it is important to include such a representation of knowledge into the process of reasoning under uncertainty. Incorporating
Target Identification Using Belief Functions and Implication Rules
, 2004
"... The paper presents the theoretical basis of data fusion for the purpose of target identification using the belief function theory. The key feature of this paper is that we allow the knowledge sources to supply their information in the form of the implication rules. The paper describes how these rule ..."
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The paper presents the theoretical basis of data fusion for the purpose of target identification using the belief function theory. The key feature of this paper is that we allow the knowledge sources to supply their information in the form of the implication rules. The paper describes how
Mining Unexpected Sequential Patterns and Implication Rules
, 2009
"... As common criteria in data mining methods, the frequency-based interestingness measures provide a statistical view of the correlation in the data, such as sequential patterns. However, when we consider domain knowledge within the mining process, the unexpected information that contradicts existing k ..."
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propose 3 forms of unexpected sequences. We further propose the notion of unexpected sequential patterns and implication rules for determining the structures and implications of the unexpectedness. The experimental results on various types of data sets show the usefulness and effectiveness of our approach.
Predictable Meaning Shift: Some Linguistic Properties of Lexical Implication Rules
, 1992
"... Drawing on a growing database of systematic relationships between word-senses, the authors argue that a significant class of these represent Lexical Implication Rules, a set of formal rules within the domain of lexical semantics; these they distinguish from other types of semantic relation more clos ..."
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Drawing on a growing database of systematic relationships between word-senses, the authors argue that a significant class of these represent Lexical Implication Rules, a set of formal rules within the domain of lexical semantics; these they distinguish from other types of semantic relation more
A Threshold Free Implication Rule Mining
"... Abstract—Typically, before association rules are mined, a user needs to determine a support threshold in order to obtain only the frequent item sets. Having users to determine a support threshold attracts a number of issues. We propose an association rule mining framework that does not require a pre ..."
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Cited by 1 (0 self)
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pre-set support threshold. The framework is developed based on implication of propositional logic. The experiments show that our approach is able to identify meaningful association rules within an acceptable execution time. Index Terms—association rule mining, propositional logic, implication
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