Titlebook: Knowledge Discovery in Databases: PKDD 2005; 9th European Confere Alípio Mário Jorge,Luís Torgo,Jo?o Gama Conference proceedings 2005 Sprin

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書目名稱Knowledge Discovery in Databases: PKDD 2005影響因子(影響力)學(xué)科排名




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書目名稱Knowledge Discovery in Databases: PKDD 2005讀者反饋




書目名稱Knowledge Discovery in Databases: PKDD 2005讀者反饋學(xué)科排名

constitutional

,-Anonymous Patternsconcept of . from data to patterns, we formally characterize the notion of a threat to anonymity in the context of pattern discovery, and provide a methodology to efficiently and effectively identify all possible such threats that might arise from the disclosure of a set of extracted patterns.

熒光

Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplicationfer to as “Kronecker graphs”..We show that Kronecker graphs naturally obey all the above properties; in fact, we can rigorously . that they do so. We also provide empirical evidence showing that they can mimic very well several real graphs.

無脊椎

Conference proceedings 2005ere jointly organized this year for the ?fth time in a row, after some years of mutual independence before. After Freiburg (2001), Helsinki (2002), Cavtat (2003) and Pisa (2004), Porto received the 16th edition of ECML and the 9th PKDD in October 3–7. Having the two conferences together seems to be

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Interestingness is Not a Dichotomy: Introducing Softness in Constrained Pattern Miningsolution, in this paper we introduce the new paradigm of pattern discovery based on .. Albeit simple, the proposed paradigm overcomes all the major methodological drawbacks of the classical constraint-based paradigm, representing an important step further towards practical pattern discovery.

Anterior

Generating Dynamic Higher-Order Markov Models in Web Usage Mining three real world data sets. The results show that some pages require a long history to understand the users choice of link, while others require only a short history. We also show that the number of additional states induced by the method can be controlled through a probability threshold parameter.

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表主動

Protein Sequence Pattern Mining with Constraintsadth or a depth first traversal are possible. The experimental evaluation, in synthetic and real life protein databases, has shown that our algorithm has superior performance to state-of-the art algorithms. The use of constraints has also proved to be a very useful tool to specify user interesting patterns.

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