New Frontiers in Mining Complex Patterns

Name: New Frontiers in Mining Complex Patterns | 4th International Workshop, NFMCP 2015, Held in Conjunction with ECML-PKDD 2015, Porto, Portugal, September 7, 2015, Revised Selected Papers
Brand: Springer
Price: 53.49 EUR
Availability: OnlineOnly

4th International Workshop, NFMCP 2015, Held in Conjunction with ECML-PKDD 2015, Porto, Portugal, September 7, 2015, Revised Selected Papers

Michelangelo Ceci Corrado Loglisci Giuseppe Manco Elio Masciari Zbigniew W. Ras(Editor)

Springer (Publisher)

Published on 17. May 2016

X, 239 pages

E-Book

PDF with digital watermarking

System requirements

978-3-319-39315-5 (ISBN)

€53.49incl. 7% vat

System requirements

for PDF with digital watermarking

E-Book Single Licence

Available for download

Description

More details

Other editions

Persons

Content

Intro
New Frontiers in Mining Complex Patterns (NFMCP 2015)
Organization
Contents
Data Stream Mining
Adaptive Ensembles for Evolving Data Streams -- Combining Block-Based and Online Solutions
1 Introduction
2 Concept Drift in Data Streams
3 Ensembles for Evolving Data Streams
4 AUE and OAUE Ensembles
4.1 Accuracy Updated Ensemble
4.2 Online Accuracy Updated Ensemble
5 Final Remarks and Open Issues
References
Comparison of Tree-Based Methods for Multi-target Regression on Data Streams
1 Introduction
2 Background and Related Work
2.1 Multi-target Regression
2.2 Data Streams
2.3 Multi-target Regression on Data Streams
3 Tree-Based Approaches for Multi-target Regression on Data Streams
3.1 A Local Approach to MTR
3.2 A Global Approach to MTR
3.3 Ensemble of Trees for MTR on Data Streams
3.4 Baseline Method
4 Experimental Setup
4.1 Experimental Questions
4.2 Evaluation Measures and Experimental Methodology
4.3 Datasets
4.4 Compared Methods
5 Results
5.1 Predictive Performance (RMAE)
5.2 Time Consumption
5.3 Memory Consumption
6 Conclusions and Further Work
References
Frequent Itemsets Mining in Data Streams Using Reconfigurable Hardware
1 Introduction
2 Theoretical Basis
2.1 Reconfigurable Computing
3 Related Works
4 A Method for Frequent Itemsets Mining Using Reconfigurable Hardware
4.1 Frequent 1-Itemsets Detection
4.2 Proposed Method
5 Results
5.1 Discussion
6 Conclusions
References
Discovering and Tracking Organizational Structures in Event Logs
1 Introduction
2 Basics
3 Time-Evolving Organization Structure Tracker
3.1 Resource Community Detection
3.2 Tracking Evolutions of Resource Communities
4 Case Studies
5 Conclusions
References
Intelligent Adaptive Ensembles for Data Stream Mining: A High Return on Investment Approach
1 Introduction
2 Background
2.1 Size of Ensemble Object in Memory
2.2 Ensemble Size versus Utility
3 Adaptive Ensemble Size Algorithm
4 Experimentation
4.1 Results
5 Conclusion
References
Mining Periodic Changes in Complex Dynamic Data Through Relational Pattern Discovery
1 Introduction
2 Basics and Definitions
3 The Method
3.1 Relational Frequent Pattern Discovery
3.2 Emerging Pattern Extraction
3.3 Periodic Change Detection
4 Experiments
5 Related Works
6 Conclusions
References
Classification
The Usefulness of Roughly Balanced Bagging for Complex and High-Dimensional Imbalanced Data
1 Introduction
2 Related Works
3 Studying the Role of Components in Roughly Balanced Bagging
3.1 Choosing Algorithms to Learn Component Classifiers
3.2 The Influence of the Number of Component Classifiers
3.3 Diversity of Component Classifiers
4 Influence of the Type of Examples
5 Applying a Random Selection of Attributes
6 Discussion and Final Remarks
References
Classifying Traces of Event Logs on the Basis of Security Risks
1 Introduction
2 Preliminaries
3 The Classification Problem and Our Approach for Solving It
3.1 The Challenges of Evaluating a Classification and Our Solution
4 The Monte Carlo Classification Algorithm
5 Experimental Validation
6 Related Work
7 Conclusions and Future Work
References
Redescription Mining with Multi-target Predictive Clustering Trees
1 Introduction
2 Notation and Definitions
3 The CLUS-RM Algorithm
3.1 The Procedure for Creating Redescriptions
3.2 Rule Size Minimization
3.3 Algorithm Time Complexity
4 Mining Redescriptions on Data Describing Countries
5 Algorithm Evaluation and Comparison
6 Conclusion
A Appendix
References
Mining Complex Data
Generalizing Patterns for Cross-Domain Analogy
1 Introduction
2 Preliminaries
3 Analogy and Inference
3.1 Representation Formalism
3.2 Analogy
3.3 Inference and Re-representation
4 Analogical Pattern Generalization
4.1 Formal Definition
4.2 Evaluation
4.3 Addition and Union
4.4 Military and Medical Strategy
4.5 Patterns Assessment
5 Conclusions
References
Spectral Features for Audio Based Vehicle Identification
1 Introduction
1.1 Related Work
1.2 Vehicle Classes
2 Data Collection and Description
3 Feature Set
3.1 Feature Selection
4 Experiments
4.1 Classifiers
4.2 Data
4.3 Classification Results
4.4 Hierarchical Classification
5 Summary and Conclusions
References
Probabilistic Frequent Subtree Kernels
1 Introduction
2 The Probabilistic Frequent Subtree Kernel
2.1 Probabilistic Frequent Subtrees
2.2 Implementation Issues and Runtime Analysis
3 Experiments
3.1 Datasets
3.2 Runtime
3.3 Recall
3.4 Stability of Probabilistic Subtree Patterns
3.5 Predictive Performance
4 Conclusion and Future Work
References
Heterogeneous Network Decomposition and Weighting with Text Mining Heuristics
1 Introduction
2 Related Work
3 Methodology
3.1 Network Decomposition
3.2 Classification
4 Methodology Improvement
4.1 Imbalanced Data Sets and Label Propagation
4.2 Text Mining Inspired Weights Calculation
5 Experimental Setting and Results
5.1 Data Set Description
5.2 Experiment Description
5.3 Experimental Results
6 Conclusions and Further Work
References
Sequences
Semi-supervised Multivariate Sequential Pattern Mining
1 Introduction
2 Related Work
3 The Semi-supervised Learning Framework
3.1 Graph Construction
3.2 Label Propagation
3.3 Extension to Out-of-Samples
4 Experimental Analysis
5 Conclusion
References
Evaluating a Simple String Representation for Intra-day Foreign Exchange Prediction
1 Introduction
2 Previous Work
3 Simple Strategy
4 String Subsequences Strategy
4.1 n-Grams
4.2 Time Decay n-Grams
5 Experiments
5.1 Simple String Strategy: Word vs. Alphabet Length
5.2 Parzen Window Strategy: Regression vs. Classification
5.3 Simple Strings Strategy vs. SVM Classification
5.4 Time Decay n-Grams: Decay Analysis
5.5 Simple String Strategy vs. Time Decay n-Grams
6 Conclusions
References
Author Index

System requirements

Save as PDF Copy link into clipboard

Schweitzer Fachinformationen

New Frontiers in Mining Complex Patterns

Description

More details

Other editions

Additional editions

Persons

Content

System requirements