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

Häftad, Engelska, 2013

AvAnnalisa Appice,Michelangelo Ceci,Corrado Loglisci,Giuseppe Manco,Elio Masciari,Zbigniew Ras

649 kr

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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.

Produktinformation

Utgivningsdatum2013-04-09
Mått155 x 235 x undefined mm
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor231
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783642373817

Tillhör följande kategorier

Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT
Databaser inom Data och IT

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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