Titlebook: New Frontiers in Mining Complex Patterns; First International Annalisa Appice,Michelangelo Ceci,Zbigniew W. Ras Conference proceedings 201

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Reducing Examples in Relational Learning with Bounded-Treewidth Hypothesesorm of clauses can be reduced in size to speed up learning .. To this end, we introduce the notion of safe reduction: a safely reduced example cannot be distinguished from the original example .. Next, we consider the particular, rather permissive bias of bounded treewidth clauses. We show that unde

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Learning in the Presence of Large Fluctuations: A Study of Aggregation and Correlation outliers. This may be the case, for instance, when predicting the potential future damages of earthquakes or oil spills, or when conducting financial data analysis. If follows that, in such a situation, the standard central limit theorem does not apply, since the associated Gaussian distribution ex

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Retracted: Machine Learning as an Objective Approach to Understanding Musicon the use of objective machine learning programs. To illustrate this methodology we investigated the distribution of music from around the world: geographical ethnomusicology. To ensure that the knowledge obtained about geographical ethnomusicology is objective and operational we cast the problem a

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Graph-Based Approaches to Clustering Network-Constrained Trajectory Datan euclidean space and did not consider the eventual presence of an underlying road network and its influence on evaluating the similarity between trajectories. In this paper, we present an approach to clustering such network-constrained trajectory data. More precisely we aim at discovering groups of

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Finding the Most Descriptive Substructures in Graphs with Discrete and Numeric Labelsn this paper we show that they can be used to improve discrimination and search performance. Our thesis is that the most descriptive substructures are those which are normative both in terms of their structure and in terms of their numeric values. We explore the relationship between graph structure