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{ "item_title" : "New Frontiers in Mining Complex Patterns", "item_author" : [" Annalisa Appice", "Michelangelo Ceci", "Corrado Loglisci "], "item_description" : "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.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/3/64/237/381/364237381X_b.jpg", "price_data" : { "retail_price" : "72.00", "online_price" : "72.00", "our_price" : "72.00", "club_price" : "72.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
New Frontiers in Mining Complex Patterns|Annalisa Appice

New Frontiers in Mining Complex Patterns : First International Workshop, Nfmcp 2012, Held in Conjunction with Ecml/Pkdd 2012, Bristol, Uk, September 24

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Overview

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.

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Details

  • ISBN-13: 9783642373817
  • ISBN-10: 364237381X
  • Publisher: Springer
  • Publish Date: April 2013
  • Dimensions: 9.21 x 6.14 x 0.51 inches
  • Shipping Weight: 0.76 pounds
  • Page Count: 231

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