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Titlebook: Discovery Science; 14th International C Tapio Elomaa,Jaakko Hollmén,Heikki Mannila Conference proceedings 2011 Springer-Verlag GmbH Berlin

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楼主: 忠诚
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Information Distance and Its Extensions,mage, an email, a webpage, a Google query, an answer, a movie, a music score, a Facebook blog, a short message, or even an abstract concept. Over the past 20 years, we have been developing a general theory of information distance in this space and applications of this theory. The theory is object-in
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Monotone Instance Ranking with ,,butes describing it. Consider, for example, the problem of ranking documents with respect to their relevance to a particular query. Typical attributes are counts of query terms in the abstract or title of the document, so it is natural to postulate the existence of an increasing relationship between
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,“Tell Me More”: Finding Related Items from User Provided Feedback,lenging problem for a user is to pick out the useful information. In this paper we propose an interactive framework to efficiently explore and (re)rank the objects retrieved by such an engine, according to feedback provided on part of the initially retrieved objects. In particular, given a set of ob
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Multiple Hypothesis Testing in Pattern Discovery,is is a very common situation in many data mining applications. For instance, assessing simultaneously the significance of all frequent itemsets of a single dataset entails a host of hypothesis, one for each itemset. A multiple hypothesis testing method is needed to control the number of false posit
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A Parameter-Free Method for Discovering Generalized Clusters in a Network,s a network. We define intuitively that generalized clusters contain at least a cluster in which nodes are connected sparsely and the cluster is connected either densely to another cluster or sparsely to another conventional cluster. The first characteristic of the MDL-based graph clustering is a di
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