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7,873
Dynamic Itemset Counting and Implication Rules for Market Basket Data
, 1997
"... We consider the problem of analyzing marketbasket 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 lowlevel 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
Mining Quantitative Association Rules in Large Relational Tables
, 1996
"... We introduce the problem of mining association rules in large relational tables containing both quantitative and categorical attributes. An example of such an association might be "10% of married people between age 50 and 60 have at least 2 cars". We deal with quantitative attributes by fi ..."
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Cited by 444 (3 self)
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"greaterthanexpectedvalue" interest measure to identify the interesting rules in the output. We give an algorithm for mining such quantitative association rules. Finally, we describe the results of using this approach on a reallife dataset. 1 Introduction Data mining, also known
Semantics of ContextFree Languages
 In Mathematical Systems Theory
, 1968
"... "Meaning " may be assigned to a string in a contextfree language by defining "attributes " of the symbols in a derivation tree for that string. The attributes can be defined 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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. An algorithm is given which detects when such semantic rules could possibly lead to circular definition of some attributes. An example is given of a simple programming language defined with both inherited and synthesized attributes, and the method of definition is compared to other techniques for formal
Knowledge Interchange Format Version 3.0 Reference Manual
, 1992
"... : Knowledge Interchange Format (KIF) is a computeroriented language for the interchange of knowledge among disparate programs. It has declarative semantics (i.e. the meaning of expressions in the representation can be understood without appeal to an interpreter for manipulating those expressions); ..."
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Cited by 484 (14 self)
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); it is logically comprehensive (i.e. it provides for the expression of arbitrary sentences in the firstorder predicate calculus); it provides for the representation of knowledge about the representation of knowledge; it provides for the representation of nonmonotonic reasoning rules; and it provides
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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. Introduction The task of calculating posterior marginals on nodes in an arbitrary Bayesian network is known to be NP hard In this paper we investigate the approximation performance of "loopy belief propagation". This refers to using the wellknown Pearl polytree algorithm [12] on a Bayesian network
Rule Induction with CN2: Some Recent Improvements
, 1991
"... The CN2 algorithm induces an ordered list of classification rules from examples using entropy as its search heuristic. In this short paper, we describe two improvements to this algorithm. Firstly, we present the use of the Laplacian error estimate as an alternative evaluation function and secondly, ..."
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Cited by 385 (2 self)
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induction, CN2, Laplace, noise 1 Introduction Rule induction from examples has established itself as a basic component of many machine learning systems, and has been the first ML technology to deliver commercially successful applications (eg. the systems GASOIL [Slocombe et al., 1986], BMT [Hayes
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
 Machine Learning
, 1998
"... . This paper addresses the problem of building largescale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximumlikelihood estimation problem. It then devises a practical algorithm for generating the most likely map from ..."
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Cited by 483 (43 self)
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data, alog with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approach. Keywords: Bayes rule, expectation maximization, mobile robots, navigation, localization, mapping, maximum likelihood
Optimality Theory
, 2000
"... Introduction Rene Kager's textbook is one of the first to cover Optimality Theory (OT), a declarative grammar framework that swiftly took over phonology after it was introduced by Prince, Smolensky, and McCarthy in 1993. OT reclaims traditional grammar's ability to express surface genera ..."
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Cited by 426 (2 self)
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Introduction Rene Kager's textbook is one of the first to cover Optimality Theory (OT), a declarative grammar framework that swiftly took over phonology after it was introduced by Prince, Smolensky, and McCarthy in 1993. OT reclaims traditional grammar's ability to express surface
A Growing Neural Gas Network Learns Topologies
 Advances in Neural Information Processing Systems 7
, 1995
"... An incremental network model is introduced which is able to learn the important topological relations in a given set of input vectors by means of a simple Hebblike learning rule. In contrast to previous approaches like the "neural gas" method of Martinetz and Schulten (1991, 1994), this m ..."
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Cited by 401 (5 self)
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An incremental network model is introduced which is able to learn the important topological relations in a given set of input vectors by means of a simple Hebblike learning rule. In contrast to previous approaches like the "neural gas" method of Martinetz and Schulten (1991, 1994
Results 1  10
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7,873