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

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New Frontiers in Mining Complex Patterns978-3-319-17876-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Mining Positional Data Streamsicable in our continuous setting. We propose an efficient trajectory-based preprocessing to identify similar movements and a distributed pattern mining algorithm to identify frequent trajectories. We empirically evaluate all parts of the processing pipeline.

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Semi-supervised Learning for Multi-target Regressioni-target regression (MTR), a?type of structured output prediction, where the output space consists of multiple numerical values. Our main objective is to investigate whether we can improve over supervised methods for MTR by using unlabeled data. We use ensembles of predictive clustering trees in a s

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Evaluation of Different Data-Derived Label Hierarchies in Multi-label Classificationy using four different clustering algorithms (balanced .-means, agglomerative clustering with single and complete linkage and predictive clustering trees). The hierarchies are then used in conjunction with global hierarchical multi-label classification (HMC) approaches. The results from the statisti

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Predicting Negative Side Effects of Surgeries Through Clustering We propose a system that measures the similarity of a new patient to existing clusters, and makes a personalized decision on the patient’s most likely negative side effects. We further evaluate our system using SID, which is part of the Healthcare Cost and Utilization Project (HCUP). Our experiment

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