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825,866
Tests of the ratio rule in categorization
 QJEP
, 2000
"... Many theories of learning and memory (e.g., connectionist, associative, rational, exemplar based) produce psychological magnitude terms as output (i.e., numbers representing the momentary level of some subjective property). Many theories assume that these numbers may be translated into choice probab ..."
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Cited by 7 (1 self)
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probabilities via the ratio rule, also known as the choice axiom (Luce, 1959) or the constantratio rule (Clarke, 1957). We present two categorization experiments employing arti®cial, visual, prototypestructured stimuli constructed from 12 symbols positionedon a grid. The ratio rule is shown to be incorrect
Quantifiable Data Mining Using Ratio Rules
"... Association Rule Mining algorithms operate on a data matrix (e.g., customers \Theta products) to derive association rules (Agrawal, Imielinski, & Swami, 1993b; Srikant & Agrawal, 1996). We propose a new paradigm, namely, Ratio Rules, which are quantifiable in that we can measure the "g ..."
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Cited by 11 (2 self)
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Association Rule Mining algorithms operate on a data matrix (e.g., customers \Theta products) to derive association rules (Agrawal, Imielinski, & Swami, 1993b; Srikant & Agrawal, 1996). We propose a new paradigm, namely, Ratio Rules, which are quantifiable in that we can measure the "
Ratio Rule Mining from Multiple Data Sources
"... Abstract. Both multiple source data mining and streaming data mining problems have attracted much attention in the past decade. In contrast to traditional associationrule mining, to capture the quantitative association knowledge, a new paradigm called Ratio Rule (RR) was proposed recently. We exten ..."
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Abstract. Both multiple source data mining and streaming data mining problems have attracted much attention in the past decade. In contrast to traditional associationrule mining, to capture the quantitative association knowledge, a new paradigm called Ratio Rule (RR) was proposed recently. We
Ratio rules: A new paradigm for fast, quantifiable data mining
 In the Proc. of the VLDB
, 1998
"... Association Rule Mining algorithms operate on a data matrix (e.g., customers products) to derive association rules [2, 23]. We propose a new paradigm, namely, Ratio Rules, which are quanti able in that we can measure the \goodness " of a set of discovered rules. We propose to use the \guessing ..."
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Cited by 26 (4 self)
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Association Rule Mining algorithms operate on a data matrix (e.g., customers products) to derive association rules [2, 23]. We propose a new paradigm, namely, Ratio Rules, which are quanti able in that we can measure the \goodness " of a set of discovered rules. We propose to use the \guessing
Mining Adaptive Ratio Rules from Distributed Data Sources
, 2005
"... Abstract. Different from traditional associationrule mining, a new paradigm called Ratio Rule (RR) was proposed recently. Ratio rules are aimed at capturing the quantitative association knowledge, We extend this framework to mining ratio rules from distributed and dynamic data sources. This is a no ..."
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Abstract. Different from traditional associationrule mining, a new paradigm called Ratio Rule (RR) was proposed recently. Ratio rules are aimed at capturing the quantitative association knowledge, We extend this framework to mining ratio rules from distributed and dynamic data sources. This is a
ThreeDimensional Brownian Motion and the Golden Ratio Rule
"... Let X = (Xt)t≥0 be a transient diffusion process in (0, ∞) with the diffusion coefficient σ> 0 and the scale function L such that Xt → ∞ as t → ∞ , let It denote its running minimum for t ≥ 0, and let θ denote the time of its ultimate minimum I ∞. Setting c(i, x) = 1−2L(x)/L(i) we show that th ..."
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Cited by 8 (3 self)
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Let X = (Xt)t≥0 be a transient diffusion process in (0, ∞) with the diffusion coefficient σ> 0 and the scale function L such that Xt → ∞ as t → ∞ , let It denote its running minimum for t ≥ 0, and let θ denote the time of its ultimate minimum I ∞. Setting c(i, x) = 1−2L(x)/L(i) we show that the stopping time τ ∗ = inf { t ≥ 0  Xt ≥ f∗(It)} minimises E(θ − τ  − θ) over all stopping times τ of X (with finite mean) where the optimal boundary f ∗ can be characterised as the minimal solution to σ 2 (f(i)) L ′ (f(i)) f ′ (i) = − c(i, f(i)) [L(f(i))−L(i)] ∫ f(i) i c ′ i (i, y) [L(y)−L(i)] σ2 (y) L ′ dy (y) staying strictly above the curve h(i) = L −1 (L(i)/2) for i> 0. In particular, when X is the radial part of threedimensional Brownian motion, we find that τ ∗ = inf t ≥ 0 ∣ Xt−It ≥ ϕ
Mining ratio rules via principal sparse nonnegative matrix factorization
 in Proc. IEEE Int. Conf. Data Mining
, 2004
"... Association rules are traditionally designed to capture statistical relationship among itemsets in a given database. To additionally capture the quantitative association knowledge, F.Korn et al recently proposed a paradigm named Ratio Rules [4] for quantifiable data mining. However, their approach i ..."
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Cited by 12 (0 self)
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Association rules are traditionally designed to capture statistical relationship among itemsets in a given database. To additionally capture the quantitative association knowledge, F.Korn et al recently proposed a paradigm named Ratio Rules [4] for quantifiable data mining. However, their approach
An experimental and theoretical investigation of the constantratio rule and other models of visual letter confusion
 Journal of Mathematical Psychology
, 1982
"... The constantratio rule (CRR) and four interpretations of R. D. Lute’s (In R. D. Lute, ..."
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Cited by 12 (1 self)
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The constantratio rule (CRR) and four interpretations of R. D. Lute’s (In R. D. Lute,
RatioRules: ANewParadigmforFast,QuantifiableDataMining FlipKorn,AlexandrosLabrinidis,YannisKotidis
"... AssociationRuleMiningalgorithmsoperate onadatamatrix(e.g.,customersproducts) toderiveassociationrules[2,23].Weproposeanewparadigm,namely,RatioRules, whicharequantiableinthatwecanmeasure the\goodness"ofasetofdiscoveredrules. Weproposetousethe\guessingerror"asa measureofthe\goodness",th ..."
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AssociationRuleMiningalgorithmsoperate onadatamatrix(e.g.,customersproducts) toderiveassociationrules[2,23].Weproposeanewparadigm,namely,RatioRules, whicharequantiableinthatwecanmeasure the\goodness"ofasetofdiscoveredrules. Weproposetousethe\guessingerror"asa measureofthe
RatioRules: ANewParadigmforFast,QuantifiableDataMining FlipKorn,AlexandrosLabrinidis,YannisKotidis
"... AssociationRuleMiningalgorithmsoperate onadatamatrix(e.g.,customersproducts) toderiveassociationrules[2,23].Weproposeanewparadigm,namely,RatioRules, whicharequantiableinthatwecanmeasure the\goodness"ofasetofdiscoveredrules. Weproposetousethe\guessingerror"asa measureofthe\goodness ..."
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AssociationRuleMiningalgorithmsoperate onadatamatrix(e.g.,customersproducts) toderiveassociationrules[2,23].Weproposeanewparadigm,namely,RatioRules, whicharequantiableinthatwecanmeasure the\goodness"ofasetofdiscoveredrules. Weproposetousethe\guessingerror"asa measureofthe
Results 1  10
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825,866