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检索条件"任意字段=7th European Conference on Principles and Practice of Knowledge Discovery in Databases"

共 85 条记录，以下是71-80 订阅

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Mining hierarchical decision rules from clinical databases using rough sets and medical diagnostic model

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6th european conference on principles and practice of knowledge discovery in databases, PKDD 2002

作者： Tsumoto, Shusaku Department of Medicine Informatics Shimane Medical University School of Medicine 89-1 Enya-cho Izumo City Shimane 693-8501 Japan

ISBN: (纸本)3540440372

One of the most important problems on rule induction methods is that they cannot extract rules, which plausibly represent experts' decision processes. On one hand, rule induction methods induce probabilistic rules, the description length of which is too short, compared with the experts' rules. On the other hand, construction of Bayesian networks generates too lengthy rules. In this paper, the characteristics of experts' rules are closely examined and a new approach to extract plausible rules is introduced, which consists of the following three procedures. First, the characterization of decision attributes (given classes) is extracted from databases and the classes are classified into several groups with respect to the characterization. then, two kinds of sub-rules, characterization rules for each group and discrimination rules for each class in the group are induced. Finally, those two parts are integrated into one rule for each decision attribute. the proposed method was evaluated on a medical database, the experimental results of which show that induced rules correctly represent experts' decision processes. © 2002 Springer-Verlag Berlin Heidelberg.

关键词： Bayesian networks

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Iteratively selecting feature subsets for mining from high-dimensional databases

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6th european conference on principles and practice of knowledge discovery in databases, PKDD 2002

作者： Mamitsuka, Hiroshi Institute for Chemical Research Kyoto University Gokasho Uji611-0011 Japan

ISBN: (纸本)3540440372

We propose a new data mining method that is effective for mining from extremely high-dimensional databases. Our proposed method iteratively selects a subset of features from a database and builds a hypothesis with the subset. Our selection of a feature subset has two steps, i.e. selecting a subset of instances from the database, to which predictions by multiple hypotheses previously obtained are most unreliable, and then selecting a subset of features, the distribution of whose values in the selected instances varies the most from that in all instances of the database. We empirically evaluate the effectiveness of the proposed method by comparing its performance with those of two other methods, including Xing et al.’s one of the latest feature subset selection methods. the evaluation was performed on a real-world data set with approximately 140,000 features. Our results show that the performance of the proposed method exceeds those of the other methods, both in terms of the final predictive accuracy and the precision attained at a recall given by Xing et al.’s method. We have also examined the effect of noise in the data and found that the advantage of the proposed method becomes more pronounced for larger noise levels. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Database systems

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4th european conference on principles and practice of knowledge discovery in databases, PKDD 2000

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4th european conference on principles and practice of knowledge discovery in databases, PKDD 2000

ISBN: (纸本)9783540410669

the proceedings contain 86 papers. the special focus in this conference is on databases, Reward-Based Learning, Classification, Association Rules and Exceptions. the topics include: Multi-relational data mining, using UML for ILP;apriori-based algorithm for mining frequent substructures from graph data;tree-simplification and knowledge discovery;combining multiple models with meta decision trees;approximation of frequency queries by means of free-sets;application of reinforcement learning to electrical power system closed-loop emergency control;efficient score-based learning of equivalence classes of BAYESIAN networks;quantifying the resilience of inductive classification algorithms;bagging and boosting with dynamic integration of classifiers;zoomed ranking: selection of classification algorithms based on relevant performance information;relative unsupervised discretization for association rule mining;unified algorithm for undirected discovery of exception rules;sampling strategies for targeting rare groups from a bank customer database;instance-based classification by emerging patterns;context-based similarity measures for categorical databases;a mixed similarity measure in near-linear computational complexity for distance-based methods;fast feature selection using partial correlation for multi-valued attributes;fast hierarchical clustering based on compressed data and optics;predictive performance of weighted relative accuracy;quality scheme assessment in the clustering process;a system for mining sets of time series;learning first order logic time series classifiers;learning right sized belief networks by means of a hybrid methodology;algorithms for mining share frequent itemsets containing infrequent subsets and inductive logic programming in Clementine.

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Determination of screening descriptors for chemical reaction databases 4th

引用

4th european conference on principles and practice of knowledge discovery in databases, PKDD 2000

作者： Dury, Laurent Leherte, Laurence Vercauteren, Daniel P. Laboratoire de Physico-Chimie Informatique Facultés Universitaires Notre-Dame de la Paix Rue de Bruxelles 61 NamurB-5000 Belgium

ISBN: (纸本)9783540410669

the development of chemical reaction databases has become crucially important for many chemical synthesis laboratories. However the size of these databases has dramatically increased, leading consequently to perfect more and more powerful search engines. In this sense, the speed and the efficiency of screening processes of the chemical information are essential criteria. Looking forward for powerful algorithms dedicated to information retrieval in chemical reaction databases, we have thus developed several new graph descriptors to find efficient indexation and classification criteria of chemical reactions. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Chemical reactions

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Discovering association rules in large, dense databases 4th

引用

4th european conference on principles and practice of knowledge discovery in databases, PKDD 2000

作者： Teusan, Tudor Nachouki, Gilles Briand, Henri Philippe, Jacques Ecole Polytechnique de l’Univ. de Nantes rue Christian Pauc Nantes Cedex 3 France Irin Faculté des Sciences et de Techniques Dép. Informatique Cedex France PerformanSe Espace Performance Atlanpole BP 703 Carquefou44481 France IUT Nantes Dép. Informatique 3 rue Maréchal Joffre Nantes Cedex44041 France

ISBN: (纸本)9783540410669

In this paper we propose an approach for mining association rules in large, dense databases. For finding such rules, frequent itemsets must first be discovered. As finding all the frequent itemsets is very time-consuming for dense databases, we propose an algorithm that is able to quickly discover an image of the complete set containing all the frequent itemsets. We define what an image is, and we present a genetic algorithm for discovering such an image. To monitor the discovery process we introduce the notion of dynamics of the algorithm. To measure the performances of our frequent itemsets discovery algorithm, we introduce the notion of efficiency of the discovery process. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Association rules

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Supporting discovery in medicine by association rule mining of bibliographic databases 4th

引用

4th european conference on principles and practice of knowledge discovery in databases, PKDD 2000

作者： Hristovski, Dimitar Džeroskf, Sašo Peterlin, Borut Rožić-Hristovski, Anamarija IBMI Medical Faculty Vrazov trg 2/2 Ljubljana1105 Slovenia Institute Jozef Stefan Jamova 39 Ljubljana1000 Slovenia Department of Human Genetics Clinical Center Ljubljana Zaloska Ljubljana1000 Slovenia Central Medical Library Medical Faculty Vrazov trg 2/2 Ljubljana1105 Slovenia

ISBN: (纸本)9783540410669

the paper presents an interactive discovery support system for the field of medicine. the intended users of the system are medical researchers. the goal of the system is: for a given starting concept of interest, discover new, potentially meaningful relations with other concepts that have not been published in the medical literature before. We performed two types of preliminary evaluation of the system: 1) by a medical doctor and 2) by automatic means. the preliminary evaluation showed that our approach for supporting discovery in medicine is promising, but also that some further work is needed, especially on limiting the number of potential discoveries the system generates. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Information services

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An efficient approach to discovering sequential patterns in large databases 4th

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4th european conference on principles and practice of knowledge discovery in databases, PKDD 2000

作者： Yen, Show-Jane Cho, Chung-Wen Department of Computer Science and Information Engineering Fu Jen Catholic University Taipei242 Taiwan

ISBN: (纸本)9783540410669

Mining sequential patterns is to discover sequential purchasing behaviors of most customers from a large amount of customer transactions. the previous approaches for mining sequential patterns need to repeatedly scan the large database, and take a large amount of computation time to find frequent sequences, which are very time consuming. In this paper, we present an algorithm SSLP to find sequential patterns, which can significantly reduce the number of the database scans. the experimental results show that our algorithms are more efficient than the other algorithms. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Sales

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Mining relational databases 4th

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4th european conference on principles and practice of knowledge discovery in databases, PKDD 2000

作者： Moal, Frédéric Turmeaux, Teddy Vrain, Christel LIFO Université d’Orléans rue Léonard de Vinci BP 6759 Orléans cedex 0245067 France

ISBN: (纸本)9783540410669

In this paper, we propose a classification system to induce an intentional definition of a relation from examples, when background kno- wledge is stored in a relational database composed of several tables and views. Refinement operators have been defined to integrate in a uniform way different induction tools learning numeric and symbolic constraints. the particularity of our approach is to use integrity constraints over the database (keys and foreign keys) to explore the hypotheses space. Moreover new attributes can be introduced, relying on the aggregation operator "group by". © Springer-Verlag Berlin Heidelberg 2000.

关键词： Classification (of information)

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Context-based similarity measures for categorical databases 4th

引用

4th european conference on principles and practice of knowledge discovery in databases, PKDD 2000

作者： Das, Gautam Mannila, Heikki Microsoft Research Nokia Research

ISBN: (纸本)9783540410669

Similarity between complex data objects is one of the central notions in data mining. We propose certain similarity (or distance) measures between various components of a 0/1 relation. We define measures between attributes, between rows, and between subrelations of the database. they find important applications in clustering, classification, and several other data mining processes. Our measures are based on the contexts of individual components. For example, two products (i.e., attributes) are deemed similar if their respective sets of customers (i.e., subrelations) are similar. this reveals more subtle relationships between components, something that is usually missing in simpler measures. Our problem of finding distance measures can be formulated as a system of nonlinear equations. We present an iterative algorithm which, when seeded with random initial values, converges quickly to stable distances in practice (typically requiring less than five iterations). the algorithm requires only one database scan. Results on artificial and real data show that our method is efficient, and produces results with intuitive appeal. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Data mining

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knowledge discovery in databases: PKDD 2006 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Johannes Fürnkranz Tobias Scheffer Myra Spiliopoulou

ISBN: (数字)9783540460480

ISBN: (纸本)9783540453741

the two premier annual european conferences in the areas of machine learning anddatamining havebeencollocatedeversincethejointconferenceinFreiburg, Germany,2001. the european conference on Machine Learning wasestablished 20 years ago, when the ?rst european Working Session on Learning was held in Orsay, France, in 1986. the conference is growing, and is more lively than ever. the european conference on principles and practice of knowledge discovery in databasescelebratesits tenth anniversary;the ?rst PKDD took place in 1997 in Trondheim, Norway. Over the years, the ECML/PKDD series has evolved into one of the largest and most selective international conferences in these areas, the only one that provides a common forum for the two closely related ?elds. In 2006, the 6th collocated ECML/PKDD took place during September 18-22, when the Humboldt-Universität zu Berlin hosted the 17th european conference on Machine Learning (ECML) and the 10th european conference on principles and practice of knowledge discovery in databases (PKDD). the successful model of a hierarchical reviewing process that was introduced last year for the ECML/PKDD 2005 in Porto has been taken over in 2006. We nominated 32 Area Chairs, each of them responsible for several closely related research topics. Suitable areas were selected on the basis of the submission s- tistics for ECML/PKDD 2005 to ensure a proper load balance among the Area Chairs. For the ?rst time, a joint Program Committee was nominated for the two conferences, consisting of 280 renowned researchers,mostly proposed by the AreaChairs.

关键词： Data Structures and Information theory Artificial Intelligence Database Management Information Storage and Retrieval Probability and Statistics in Computer Science Natural Language Processing (NLP)

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