Overview

This book constitutes the thoroughly refereed conference proceedings of the First International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2012, held in conjunction with ECML/PKDD 2012, in Bristol, UK, in September 2012. The 15 revised full papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on mining rich (relational) datasets, mining complex patterns from miscellaneous data, mining complex patterns from trajectory and sequence data, and mining complex patterns from graphs and networks.

Full Product Details

Author:   Annalisa Appice ,  Michelangelo Ceci ,  Corrado Loglisci ,  Giuseppe Manco
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   2013 ed.
Volume:   7765
Dimensions:   Width: 15.50cm , Height: 1.30cm , Length: 23.50cm
Weight:   0.379kg
ISBN:  

9783642373817

ISBN 10:   364237381
Pages:   231
Publication Date:   09 April 2013
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

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-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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