New Frontiers in Mining Complex Patterns

Name: New Frontiers in Mining Complex Patterns | First International Workshop, NFMCP 2012, Held in Conjunction with ECML/PKDD 2012, Bristol, UK, September 24, 2012, Revised Selected Papers
Brand: Springer
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First International Workshop, NFMCP 2012, Held in Conjunction with ECML/PKDD 2012, Bristol, UK, September 24, 2012, Revised Selected Papers

Annalisa Appice Michelangelo Ceci Corrado Loglisci Giuseppe Manco Elio Masciari Zbigniew Ras(Herausgeber*in)

Springer (Verlag)

Erschienen am 25. März 2013

X, 231 Seiten

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Learning with Configurable Operators and RL-Based Heuristics.- Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses.- Mining Complex Event Patterns in Computer Networks.- Learning in the Presence of Large Fluctuations: A Study of Aggregation and Correlation.- Machine Learning as an Objective Approach to Understanding Music.- Pair-Based Object-Driven Action Rules.- Effectively Grouping Trajectory Streams.- Healthcare Trajectory Mining by Combining Multidimensional Component and Itemsets.- Graph-Based Approaches to Clustering Network-Constrained Trajectory Data.- Finding the Most Descriptive Substructures in Graphs with Discrete and Numeric Labels.- Learning in Probabilistic Graphs Exploiting Language-Constrained Patterns.- Improving Robustness and Flexibility of Concept Taxonomy Learning from Text.- Discovering Evolution Chains in Dynamic Networks.- Supporting Information Spread in a Social Internetworking Scenario.- Context-Aware Predictions on Business Processes: An Ensemble-Based Solution.

Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses.- Mining Complex Event Patterns in Computer Networks.- Learning in the Presence of Large Fluctuations: A Study of Aggregation and Correlation.- Machine Learning as an Objective Approach to Understanding Music.- Pair-Based Object-Driven Action Rules.- Effectively Grouping Trajectory Streams.- Healthcare Trajectory Mining by Combining Multidimensional Component and Itemsets.- Graph-Based Approaches to Clustering Network-Constrained Trajectory Data.- Finding the Most Descriptive Substructures in Graphs with Discrete and Numeric Labels.- Learning in Probabilistic Graphs Exploiting Language-ConstrainedPatterns.- Improving Robustness and Flexibility of Concept Taxonomy Learning from Text.- Discovering Evolution Chains in Dynamic Networks.- Supporting Information Spread in a Social Internetworking Scenario.- Context-Aware Predictions on Business Processes: An Ensemble-Based Solution.

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