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Mining Association Rules between Sets of Items in Large Databases
 IN: PROCEEDINGS OF THE 1993 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, WASHINGTON DC (USA
, 1993
"... We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel esti ..."
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Cited by 2418 (15 self)
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We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
Theory Revision in Equation Discovery
, 2001
"... State of the art equation discovery systems start the discovery process from scratch, rather than from an initial hypothesis in the space of equations. On the other hand, theory revision systems start from a given theory as an initial hypothesis and use new examples to improve its quality. Two quali ..."
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Cited by 8 (0 self)
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State of the art equation discovery systems start the discovery process from scratch, rather than from an initial hypothesis in the space of equations. On the other hand, theory revision systems start from a given theory as an initial hypothesis and use new examples to improve its quality. Two quality criteria are usually used in theory revision systems.
Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery
, 2001
"... State of the art equation discovery systems are concerned with the empirical approach to modeling of physical systems, where none or a very limited portion of the expert knowledge about the observed system is used in the modeling process. In this paper, we propose a formalism for integration of the ..."
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Cited by 8 (1 self)
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State of the art equation discovery systems are concerned with the empirical approach to modeling of physical systems, where none or a very limited portion of the expert knowledge about the observed system is used in the modeling process. In this paper, we propose a formalism for integration of the population dynamics modeling knowledge into the process of equation discovery. The formalism allows the encoding of a highlevel domain knowledge accessible to human experts. The encoded knowledge can be automatically transformed into the operational form of context dependent grammars. We present an extended version of the equation discovery system Lagramge that can use these context free grammars. Experimental evaluation shows that the integration of domain knowledge in the process of equation discovery considerably improves the eciency and noise robustness of Lagramge.
Understanding complex systems through examples: A framework for qualitative example finding
 Kingston University
, 2000
"... Many complex systems have the characteristic that we can classify objects in the system in some way, but that these classi cations are distributed through a parameter space in some complex fashion. In order for a human to get an understanding of the system, we would like to present this user with on ..."
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Cited by 3 (2 self)
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Many complex systems have the characteristic that we can classify objects in the system in some way, but that these classi cations are distributed through a parameter space in some complex fashion. In order for a human to get an understanding of the system, we would like to present this user with one example of an object for each class. Examples of such problems can be found in information retrieval, bioinformatics, computational geometry, computeraided design, software testing and cellular automata. In this paper we will show how problems in all these areas can be put into a general framework of nding qualitative examples, and argue that general heuristic approaches to this type of problem are an important and neglected area of machine learning. We contrast this with some other wellstudied problems, showing how this problem is distinct and investigating what we can learn from these problems. We then discuss some of the requirements for a heuristic to solve these problems,...
GloBo: un algorithme stochastique pour . . .
"... Il est maintenant acquis que de tres fortes relations existent entre l'apprentissage supervise et nonsupervise [4, 14]. L'approche decrite dans ce papier est basee sur l'idee que le supervise et le nonsupervise peuvent ^etre vus comme les instances particulieres d'un probleme plus general de couve ..."
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Il est maintenant acquis que de tres fortes relations existent entre l'apprentissage supervise et nonsupervise [4, 14]. L'approche decrite dans ce papier est basee sur l'idee que le supervise et le nonsupervise peuvent ^etre vus comme les instances particulieres d'un probleme plus general de couverture minimale. Le systeme GloBo a pour objectif de caracteriser les sousconcepts veri ant une propriete particuliere: la correction par rapport aux exemples negatifs dans le cas du supervise, et une speci cite su sante en nonsupervise. Ce systeme est stochastique et travaille en temps polyn^omial. Les experimentations menees sur des problemes bien connus, en supervise comme en nonsupervise, valident notre approche.