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Complex Pattern Mining : New Challenges, Methods and Applications / / edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras
| Complex Pattern Mining : New Challenges, Methods and Applications / / edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (x, 250 pages) : illustrations |
| Disciplina | 006.3 |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Artificial intelligence Data mining Pattern recognition systems Computational Intelligence Artificial Intelligence Data Mining and Knowledge Discovery Automated Pattern Recognition |
| ISBN | 3-030-36617-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Efficient Infrequent Pattern Mining using Negative Itemset Tree -- Hierarchical Adversarial Training for Multi-Domain -- Optimizing C-index via Gradient Boosting in Medical Survival Analysis -- Order-preserving Biclustering Based on FCA and Pattern Structures -- A text-based regression approach to predict bug-fix time -- A Named Entity Recognition Approach for Albanian Using Deep Learning -- A Latitudinal Study on the Use of Sequential and Concurrency Patterns in Deviance Mining -- Efficient Declarative-based Process Mining using an Enhanced Framework -- Exploiting Pattern Set Dissimilarity for Detecting Changes in Communication Networks -- Classification and Clustering of Emotive Microblogs in Albanian: Two User-Oriented Tasks. |
| Record Nr. | UNINA-9910484953003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
New Frontiers in Mining Complex Patterns [[electronic resource] ] : 8th International Workshop, NFMCP 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Revised Selected Papers / / edited by Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew Ras
| New Frontiers in Mining Complex Patterns [[electronic resource] ] : 8th International Workshop, NFMCP 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Revised Selected Papers / / edited by Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew Ras |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xii, 155 pages) : illustrations |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Data mining Computer communication systems Architecture, Computer Application software Education—Data processing Artificial Intelligence Data Mining and Knowledge Discovery Computer Communication Networks Computer System Implementation Computer Applications Computers and Education |
| ISBN | 3-030-48861-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A Framework for Pattern Mining and Anomaly Detection in Multi-Dimensional Time Series and Event Logs -- A Heuristic Approach for Sensitive Pattern Hiding with Improved Data Quality -- Interpretable Survival Gradient Boosting Models with Bagged Trees Base Learners -- Neural Hybrid Recommender: Recommendation Needs Collaboration -- Discovering Discriminative Nodes for Classification with Deep Graph Convolutional Methods -- Soft Voting Windowing Ensembles for Learning from Partially Labelled Streams -- Disentangling Aspect and Opinion Words in Sentiment Analysis Using Lifelong PU Learning -- Customer Purchase Behavior Prediction in E-commerce: A Conceptual Framework and Research Agenda -- Hough Transform as a Tool for the Classification of Vehicle Speed Changes in on-road Audio Recordings. |
| Record Nr. | UNISA-996418318703316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
New Frontiers in Mining Complex Patterns : 8th International Workshop, NFMCP 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Revised Selected Papers / / edited by Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew Ras
| New Frontiers in Mining Complex Patterns : 8th International Workshop, NFMCP 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Revised Selected Papers / / edited by Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew Ras |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xii, 155 pages) : illustrations |
| Disciplina |
006.3
006.312 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Data mining Computer networks Computer systems Application software Education—Data processing Artificial Intelligence Data Mining and Knowledge Discovery Computer Communication Networks Computer System Implementation Computer and Information Systems Applications Computers and Education |
| ISBN | 3-030-48861-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A Framework for Pattern Mining and Anomaly Detection in Multi-Dimensional Time Series and Event Logs -- A Heuristic Approach for Sensitive Pattern Hiding with Improved Data Quality -- Interpretable Survival Gradient Boosting Models with Bagged Trees Base Learners -- Neural Hybrid Recommender: Recommendation Needs Collaboration -- Discovering Discriminative Nodes for Classification with Deep Graph Convolutional Methods -- Soft Voting Windowing Ensembles for Learning from Partially Labelled Streams -- Disentangling Aspect and Opinion Words in Sentiment Analysis Using Lifelong PU Learning -- Customer Purchase Behavior Prediction in E-commerce: A Conceptual Framework and Research Agenda -- Hough Transform as a Tool for the Classification of Vehicle Speed Changes in on-road Audio Recordings. |
| Record Nr. | UNINA-9910409664803321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
New Frontiers in Mining Complex Patterns [[electronic resource] ] : 6th International Workshop, NFMCP 2017, Held in Conjunction with ECML-PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Revised Selected Papers / / edited by Annalisa Appice, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras
| New Frontiers in Mining Complex Patterns [[electronic resource] ] : 6th International Workshop, NFMCP 2017, Held in Conjunction with ECML-PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Revised Selected Papers / / edited by Annalisa Appice, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XII, 197 p. 57 illus.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Arithmetic and logic units, Computer Application software Artificial intelligence Data Mining and Knowledge Discovery Arithmetic and Logic Structures Computer Appl. in Social and Behavioral Sciences Artificial Intelligence |
| ISBN | 3-319-78680-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Learning Association Rules for Pharmacogenomic Studies -- Segment-Removal Based Stuttered Speech Remediation -- Identifying lncRNA-disease Relationships via Heterogeneous Clustering -- Density Estimators for Positive-Unlabeled Learning -- Combinatorial Optimization Algorithms to Mine a Sub-Matrix of Maximal Sum -- A Scaled-Correlation Based Approach for Defining and analyzing functional networks -- Complex Localization in the Multiple Instance Learning Context -- Integrating a Framework for Discovering Alternative App Stores in a Mobile App Monitoring Platform -- Usefulness of Unsupervised Ensemble Learning Methods for Time Series Forecasting of Aggregated or Clustered Load -- Phenotype Prediction with Semi-supervised Classification Trees -- Structuring the Output Space in Multi-label Classification by Using Feature Ranking -- Infinite Mixtures of Markov Chains -- Community-based Semantic Subgroup Discovery. |
| Record Nr. | UNISA-996465525603316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
New Frontiers in Mining Complex Patterns : 6th International Workshop, NFMCP 2017, Held in Conjunction with ECML-PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Revised Selected Papers / / edited by Annalisa Appice, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras
| New Frontiers in Mining Complex Patterns : 6th International Workshop, NFMCP 2017, Held in Conjunction with ECML-PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Revised Selected Papers / / edited by Annalisa Appice, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XII, 197 p. 57 illus.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Computer arithmetic and logic units Social sciences—Data processing Artificial intelligence Data Mining and Knowledge Discovery Arithmetic and Logic Structures Computer Application in Social and Behavioral Sciences Artificial Intelligence |
| ISBN | 3-319-78680-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Learning Association Rules for Pharmacogenomic Studies -- Segment-Removal Based Stuttered Speech Remediation -- Identifying lncRNA-disease Relationships via Heterogeneous Clustering -- Density Estimators for Positive-Unlabeled Learning -- Combinatorial Optimization Algorithms to Mine a Sub-Matrix of Maximal Sum -- A Scaled-Correlation Based Approach for Defining and analyzing functional networks -- Complex Localization in the Multiple Instance Learning Context -- Integrating a Framework for Discovering Alternative App Stores in a Mobile App Monitoring Platform -- Usefulness of Unsupervised Ensemble Learning Methods for Time Series Forecasting of Aggregated or Clustered Load -- Phenotype Prediction with Semi-supervised Classification Trees -- Structuring the Output Space in Multi-label Classification by Using Feature Ranking -- Infinite Mixtures of Markov Chains -- Community-based Semantic Subgroup Discovery. |
| Record Nr. | UNINA-9910349425303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
New Frontiers in Mining Complex Patterns [[electronic resource] ] : 5th International Workshop, NFMCP 2016, Held in Conjunction with ECML-PKDD 2016, Riva del Garda, Italy, September 19, 2016, Revised Selected Papers / / edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Elio Masciari, Zbigniew W. Raś
| New Frontiers in Mining Complex Patterns [[electronic resource] ] : 5th International Workshop, NFMCP 2016, Held in Conjunction with ECML-PKDD 2016, Riva del Garda, Italy, September 19, 2016, Revised Selected Papers / / edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Elio Masciari, Zbigniew W. Raś |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XIV, 263 p. 66 illus.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Database management Information storage and retrieval Artificial intelligence Data Mining and Knowledge Discovery Database Management Information Storage and Retrieval Artificial Intelligence |
| ISBN | 3-319-61461-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Feature selection and induction -- Classification and prediction -- Clustering -- Pattern discovery -- Applications. |
| Record Nr. | UNISA-996466455203316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
New Frontiers in Mining Complex Patterns : 5th International Workshop, NFMCP 2016, Held in Conjunction with ECML-PKDD 2016, Riva del Garda, Italy, September 19, 2016, Revised Selected Papers / / edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Elio Masciari, Zbigniew W. Raś
| New Frontiers in Mining Complex Patterns : 5th International Workshop, NFMCP 2016, Held in Conjunction with ECML-PKDD 2016, Riva del Garda, Italy, September 19, 2016, Revised Selected Papers / / edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Elio Masciari, Zbigniew W. Raś |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XIV, 263 p. 66 illus.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Database management Information storage and retrieval systems Artificial intelligence Data Mining and Knowledge Discovery Database Management Information Storage and Retrieval Artificial Intelligence |
| ISBN | 3-319-61461-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Feature selection and induction -- Classification and prediction -- Clustering -- Pattern discovery -- Applications. |
| Record Nr. | UNINA-9910484806103321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
New Frontiers in Mining Complex Patterns [[electronic resource] ] : 4th International Workshop, NFMCP 2015, Held in Conjunction with ECML-PKDD 2015, Porto, Portugal, September 7, 2015, Revised Selected Papers / / edited by Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras
| New Frontiers in Mining Complex Patterns [[electronic resource] ] : 4th International Workshop, NFMCP 2015, Held in Conjunction with ECML-PKDD 2015, Porto, Portugal, September 7, 2015, Revised Selected Papers / / edited by Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (X, 239 p. 57 illus.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Database management Information storage and retrieval Artificial intelligence Data Mining and Knowledge Discovery Database Management Information Storage and Retrieval Artificial Intelligence |
| ISBN | 3-319-39315-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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. |
| Record Nr. | UNISA-996465681503316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
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 / / edited by Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras
| 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 / / edited by Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (X, 239 p. 57 illus.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Database management Information storage and retrieval systems Artificial intelligence Data Mining and Knowledge Discovery Database Management Information Storage and Retrieval Artificial Intelligence |
| ISBN | 3-319-39315-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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. |
| Record Nr. | UNINA-9910484458003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
New Frontiers in Mining Complex Patterns [[electronic resource] ] : Third International Workshop, NFMCP 2014, Held in Conjunction with ECML-PKDD 2014, Nancy, France, September 19, 2014, Revised Selected Papers / / edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras
| New Frontiers in Mining Complex Patterns [[electronic resource] ] : Third International Workshop, NFMCP 2014, Held in Conjunction with ECML-PKDD 2014, Nancy, France, September 19, 2014, Revised Selected Papers / / edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (XII, 211 p. 61 illus.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data mining
Database management Information storage and retrieval Artificial intelligence Data Mining and Knowledge Discovery Database Management Information Storage and Retrieval Artificial Intelligence |
| ISBN | 3-319-17876-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Sampling and Presenting Patterns from Structured Data -- Contents -- Classification and Regression -- Semi-supervised Learning for Multi-target Regression -- 1 Introduction -- 2 MTR with Ensembles of Predictive Clustering Trees -- 2.1 Predictive Clustering Trees for MTR -- 2.2 Ensembles of PCTs -- 3 Self-training for MTR with Ensembles of PCTs -- 4 Experimental Design -- 4.1 Data Description -- 4.2 Experimental Setup and Evaluation Procedure -- 5 Results and Discussion -- 6 Conclusions -- References -- Evaluation of Different Data-Derived Label Hierarchies in Multi-label Classification -- 1 Introduction -- 2 Background -- 2.1 The Task of Multi-label Classification (MLC) -- 2.2 The Task of Hierarchical Multi-label Classification (HMC) -- 3 The Use of Data Derived Label Hierarchies in Multi-Label Classification -- 3.1 Generating a Label Hierarchy on a Multi-label Output Space -- 3.2 Solving MLC Problems by Using Classification Approaches for HMC -- 3.3 Classification Approaches for HMC -- 4 Experimental Design -- 4.1 Datasets and Evaluation Measures -- 4.2 Experimental Setup -- 4.3 Statistical Evaluation -- 5 Results and Discussion -- 6 Conclusions and Further Work -- A Evaluation Measures -- A.1 Example Based Measures -- A.2 Label Based Measures -- A.3 Ranking Based Measures -- B Complete Results from the Experimental Evaluation -- References -- Clustering -- Predicting Negative Side Effects of Surgeries Through Clustering -- 1 Introduction -- 2 Background -- 2.1 Multi-valued Information System -- 2.2 Atomic Action Terms and Action Terms -- 2.3 Meta-Actions for Multi-valued Information System -- 3 Negative Side Effects -- 4 Clustering Based on Negative Side Effects -- 5 New Approach for Predicting Negative Side Effects -- 5.1 Distance Between Two Patients -- 5.2 Distance Between a Patient and a Cluster.
6 Dataset and Experiments -- 6.1 HCUP Dataset Description -- 6.2 Experiments -- 7 Summary and Conclusions -- References -- Parallel Multicut Segmentation via Dual Decomposition -- 1 Introduction -- 2 Related Work -- 3 Segmentations and Multicuts -- 4 Outer Relaxation of the Cycle Polytope -- 5 Lagrangian Decomposition -- 5.1 Constrained Reparameterization -- 6 Bound Maximization Along Subgradients -- 7 Rounding Heuristic and Interpretation -- 7.1 Decoding Heuristic: Iterative Construction -- 8 Experiments -- 8.1 Berkeley Segmentation Data Set -- 8.2 Correlation Clustering in Non-planar Graphs -- 9 Discussion -- References -- Learning from Imbalanced Data Using Ensemble Methods and Cluster-Based Undersampling -- 1 Introduction -- 2 Related Work -- 3 Proposed Algorithms -- 3.1 Undersampling Based on Clustering and K-Nearest Neighbour -- 3.2 Undersampling Based on Clustering and Ensemble Learning -- 4 Experiments and Results -- 4.1 Evaluation Criteria -- 4.2 Datasets and Experimental Settings -- 4.3 Results and Analyses -- 5 Conclusion and Future Work -- References -- Data Streams and Sequences -- Prequential AUC for Classifier Evaluation and Drift Detection in Evolving Data Streams -- 1 Introduction -- 2 Evaluating Data Stream Classifiers -- 3 Prequential AUC -- 4 Drift Detection Using AUC -- 5 Experiments -- 5.1 Datasets -- 5.2 Results -- 6 Conclusions -- References -- Mining Positional Data Streams -- 1 Introduction -- 2 Related Work -- 3 Efficiently Finding Similar Movements -- 3.1 Representation -- 3.2 Approximate Dynamic Time Warping -- 3.3 An N-Best Algorithm -- 3.4 Distance-Based Hashing -- 4 Frequent Episode Mining for Positional Data -- 4.1 Counting Phase -- 4.2 Generation Phase -- 5 Empirical Evaluation -- 5.1 Positional Data -- 5.2 Near Neighbour Search -- 5.3 Episode Discovery -- 6 Conclusion -- References. Visualization for Streaming Telecommunications Networks -- 1 Motivation -- 2 Related Work -- 2.1 Visualization -- 2.2 top-k Itemsets -- 3 Streaming Simulation System -- 3.1 Components -- 3.2 Landmark Windows -- 3.3 Sliding Windows -- 3.4 top-k Networks -- 4 Case Study -- 4.1 Data Description -- 4.2 Sliding Windows Visualization -- 4.3 top-k Landmark Window -- 5 Conclusions -- References -- Temporal Dependency Detection Between Interval-Based Event Sequences -- 1 Introduction -- 2 Temporal Dependencies -- 2.1 Temporal Dependency Assessment -- 2.2 Significant Temporal Dependencies Selection -- 3 Discovery of Temporal Dependencies -- 4 Experimental Study -- 4.1 Quantitative Experiments -- 4.2 Case Study -- 5 Related Work -- 6 Conclusion -- References -- Applications -- Discovering Behavioural Patterns in Knowledge-Intensive Collaborative Processes -- 1 Introduction -- 1.1 Motivation -- 2 Related Work -- 3 Behavioural Pattern -- 4 Methodology -- 4.1 Case Study: Collaborative Research Activity -- 4.2 Log Building -- 4.3 Hierarchical Clustering -- 5 Experiments -- 5.1 Discussion -- 6 Conclusions and Future Work -- References -- Learning Complex Activity Preconditions in Process Mining -- 1 Introduction -- 2 Representation -- 3 Learning -- 4 Computational Complexity Issues -- 5 Exploitation -- 6 Evaluation -- 7 Conclusions -- References -- Location Prediction of Mobile Phone Users Using Apriori-Based Sequence Mining with Multiple Support Thresholds -- 1 Introduction -- 2 Previous Work -- 3 Proposed Technique -- 3.1 Preliminaries -- 3.2 Apriori-Based Sequence Mining Algorithm with Multiple Support Thresholds (ASMAMS) -- 4 Evaluation -- 5 Experimental Results -- 6 Conclusion -- References -- Pitch-Related Identification of Instruments in Classical Music Recordings -- 1 Introduction -- 2 Audio Data -- 2.1 Parametrization -- 3 Classification with Random Forests. 3.1 Instrument and Pitch Identification -- 3.2 Cleaning -- 3.3 Training of Random Forests -- 4 Results -- 5 Summary and Conclusions -- References -- Author Index. |
| Record Nr. | UNISA-996207294703316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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