New Frontiers in Mining Complex Patterns [E-Book] : First International Workshop, NFMCP 2012, Held in Conjunction with ECML/PKDD 2012, Bristol, UK, September 24, 2012, Rivesed Selected Papers / edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras.
Appice, Annalisa.
Ceci, Michelangelo. / Loglisci, Corrado. / Manco, Giuseppe. / Masciari, Elio. / Ras, Zbigniew W.
Berlin, Heidelberg : Springer, 2013
X, 231 p. 57 illus. digital.
englisch
Printed edition: 9783642373817
9783642373824
10.1007/978-3-642-37382-4
Lecture notes in computer science ; 7765
Full Text
Table of Contents:
  • 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-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. .