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Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings / / edited by Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang
| Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings / / edited by Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang |
| Edizione | [1st ed. 2004.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2004 |
| Descrizione fisica | 1 online resource (XIX, 716 p.) |
| Disciplina | 005.74 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data structures (Computer science)
Artificial intelligence Database management Information storage and retrieval Multimedia information systems Mathematical statistics Data Structures and Information Theory Artificial Intelligence Database Management Information Storage and Retrieval Multimedia Information Systems Probability and Statistics in Computer Science |
| ISBN |
1-280-30807-9
9786610308071 3-540-24775-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Speeches -- Session 1A: Classification (I) -- Session 1B: Clustering (I) -- Session 1C: Association Rules (I) -- Session 2A: Novel Algorithms (I) -- Session 2B: Association (II) -- Session 2C: Classification (II) -- Session 3A: Event Mining, Anomaly Detection, and Intrusion Detection -- Session 3B: Ensemble Learning -- Session 3C: Bayesian Network and Graph Mining -- Session 3D: Text Mining (I) -- Session 4A: Clustering (II) -- Session 4B: Association (III) -- Session 4C: Novel Algorithms (II) -- Session 4D: Multimedia Mining -- Session 5A: Text Mining and Web Mining (II) -- Session 5B: Statistical Methods, Sequential Data Mining, and Time Series Mining -- Session 5C: Novel Algorithms (III) -- Session 5D: Biomedical Mining. |
| Record Nr. | UNISA-996465581003316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings / / edited by Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang
| Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings / / edited by Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang |
| Edizione | [1st ed. 2004.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2004 |
| Descrizione fisica | 1 online resource (XIX, 716 p.) |
| Disciplina | 005.74 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data structures (Computer science)
Artificial intelligence Database management Information storage and retrieval Multimedia information systems Mathematical statistics Data Structures and Information Theory Artificial Intelligence Database Management Information Storage and Retrieval Multimedia Information Systems Probability and Statistics in Computer Science |
| ISBN |
1-280-30807-9
9786610308071 3-540-24775-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Speeches -- Session 1A: Classification (I) -- Session 1B: Clustering (I) -- Session 1C: Association Rules (I) -- Session 2A: Novel Algorithms (I) -- Session 2B: Association (II) -- Session 2C: Classification (II) -- Session 3A: Event Mining, Anomaly Detection, and Intrusion Detection -- Session 3B: Ensemble Learning -- Session 3C: Bayesian Network and Graph Mining -- Session 3D: Text Mining (I) -- Session 4A: Clustering (II) -- Session 4B: Association (III) -- Session 4C: Novel Algorithms (II) -- Session 4D: Multimedia Mining -- Session 5A: Text Mining and Web Mining (II) -- Session 5B: Statistical Methods, Sequential Data Mining, and Time Series Mining -- Session 5C: Novel Algorithms (III) -- Session 5D: Biomedical Mining. |
| Record Nr. | UNINA-9910768468903321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Knowledge Discovery and Data Mining : 7th Pacific-Asia Conference, PAKDD 2003. Seoul, Korea, April 30 - May 2, 2003, Proceedings / / edited by Kyu-Young Whang, Jongwoo Jeon, Kyuseok Shim, Jaideep Srivatava
| Advances in Knowledge Discovery and Data Mining : 7th Pacific-Asia Conference, PAKDD 2003. Seoul, Korea, April 30 - May 2, 2003, Proceedings / / edited by Kyu-Young Whang, Jongwoo Jeon, Kyuseok Shim, Jaideep Srivatava |
| Edizione | [1st ed. 2003.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003 |
| Descrizione fisica | 1 online resource (XVIII, 614 p.) |
| Disciplina | 005.74 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data structures (Computer science)
Artificial intelligence Mathematical statistics Database management Information storage and retrieval Application software Data Structures and Information Theory Artificial Intelligence Probability and Statistics in Computer Science Database Management Information Storage and Retrieval Information Systems Applications (incl. Internet) |
| ISBN | 3-540-36175-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Industrial Papers (Invited) -- Data Mining as an Automated Service -- Trends and Challenges in the Industrial Applications of KDD -- Stream Mining I -- Finding Event-Oriented Patterns in Long Temporal Sequences -- Mining Frequent Episodes for Relating Financial Events and Stock Trends -- Graph Mining -- An Efficient Algorithm of Frequent Connected Subgraph Extraction -- Classifier Construction by Graph-Based Induction for Graph-Structured Data -- Clustering I -- Comparison of the Performance of Center-Based Clustering Algorithms -- Automatic Extraction of Clusters from Hierarchical Clustering Representations -- Text Mining -- Large Scale Unstructured Document Classification Using Unlabeled Data and Syntactic Information -- Extracting Shared Topics of Multiple Documents -- An Empirical Study on Dimensionality Optimization in Text Mining for Linguistic Knowledge Acquisition -- A Semi-supervised Algorithm for Pattern Discovery in Information Extraction from Textual Data -- Bio Mining -- Mining Patterns of Dyspepsia Symptoms Across Time Points Using Constraint Association Rules -- Predicting Protein Structural Class from Closed Protein Sequences -- Learning Rules to Extract Protein Interactions from Biomedical Text -- Predicting Protein Interactions in Human by Homologous Interactions in Yeast -- Web Mining -- Mining the Customer’s Up-To-Moment Preferences for E-commerce Recommendation -- A Graph-Based Optimization Algorithm for Website Topology Using Interesting Association Rules -- A Markovian Approach for Web User Profiling and Clustering -- Extracting User Interests from Bookmarks on the Web -- Stream Mining II -- Mining Frequent Instances on Workflows -- Real Time Video Data Mining for Surveillance Video Streams -- Distinguishing Causal and Acausal Temporal Relations -- Bayesian Networks -- Online Bayes Point Machines -- Exploiting Hierarchical Domain Values for Bayesian Learning -- A New Restricted Bayesian Network Classifier -- Clustering II -- AGRID: An Efficient Algorithm for Clustering Large High-Dimensional Datasets -- Multi-level Clustering and Reasoning about Its Clusters Using Region Connection Calculus -- An Efficient Cell-Based Clustering Method for Handling Large, High-Dimensional Data -- Association Rules I -- Enhancing SWF for Incremental Association Mining by Itemset Maintenance -- Reducing Rule Covers with Deterministic Error Bounds -- Evolutionary Approach for Mining Association Rules on Dynamic Databases -- Semi-structured Data Mining -- Position Coded Pre-order Linked WAP-Tree for Web Log Sequential Pattern Mining -- An Integrated System of Mining HTML Texts and Filtering Structured Documents -- A New Sequential Mining Approach to XML Document Similarity Computation -- Classification I -- Optimization of Fuzzy Rules for Classification Using Genetic Algorithm -- Fast Pattern Selection for Support Vector Classifiers -- Averaged Boosting: A Noise-Robust Ensemble Method -- Improving Performance of Decision Tree Algorithms with Multi-edited Nearest Neighbor Rule -- Data Analysis -- HOT: Hypergraph-Based Outlier Test for Categorical Data -- A Method for Aggregating Partitions, Applications in K.D.D. -- Efficiently Computing Iceberg Cubes with Complex Constraints through Bounding -- Extraction of Tag Tree Patterns with Contractible Variables from Irregular Semistructured Data -- Association Rules II -- Step-by-Step Regression: A More Efficient Alternative for Polynomial Multiple Linear Regression in Stream Cube -- Progressive Weighted Miner: An Efficient Method for Time-Constraint Mining -- Mining Open Source Software (OSS) Data Using Association Rules Network -- Parallel FP-Growth on PC Cluster -- Feature Selection -- Active Feature Selection Using Classes -- Electricity Based External Similarity of Categorical Attributes -- Weighted Proportional k-Interval Discretization for Naive-Bayes Classifiers -- Dealing with Relative Similarity in Clustering: An Indiscernibility Based Approach -- Stream Mining III -- Considering Correlation between Variables to Improve Spatiotemporal Forecasting -- Correlation Analysis of Spatial Time Series Datasets: A Filter-and-Refine Approach -- When to Update the Sequential Patterns of Stream Data? -- Clustering III -- A New Clustering Algorithm for Transaction Data via Caucus -- DBRS: A Density-Based Spatial Clustering Method with Random Sampling -- Optimized Clustering for Anomaly Intrusion Detection -- Classification II -- Finding Frequent Subgraphs from Graph Structured Data with Geometric Information and Its Application to Lossless Compression -- Upgrading ILP Rules to First-Order Bayesian Networks -- A Clustering Validity Assessment Index. |
| Altri titoli varianti | PAKDD '03 |
| Record Nr. | UNINA-9910143873203321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Knowledge Discovery and Data Mining : 7th Pacific-Asia Conference, PAKDD 2003. Seoul, Korea, April 30 - May 2, 2003, Proceedings / / edited by Kyu-Young Whang, Jongwoo Jeon, Kyuseok Shim, Jaideep Srivatava
| Advances in Knowledge Discovery and Data Mining : 7th Pacific-Asia Conference, PAKDD 2003. Seoul, Korea, April 30 - May 2, 2003, Proceedings / / edited by Kyu-Young Whang, Jongwoo Jeon, Kyuseok Shim, Jaideep Srivatava |
| Edizione | [1st ed. 2003.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003 |
| Descrizione fisica | 1 online resource (XVIII, 614 p.) |
| Disciplina | 005.74 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data structures (Computer science)
Artificial intelligence Mathematical statistics Database management Information storage and retrieval Application software Data Structures and Information Theory Artificial Intelligence Probability and Statistics in Computer Science Database Management Information Storage and Retrieval Information Systems Applications (incl. Internet) |
| ISBN | 3-540-36175-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Industrial Papers (Invited) -- Data Mining as an Automated Service -- Trends and Challenges in the Industrial Applications of KDD -- Stream Mining I -- Finding Event-Oriented Patterns in Long Temporal Sequences -- Mining Frequent Episodes for Relating Financial Events and Stock Trends -- Graph Mining -- An Efficient Algorithm of Frequent Connected Subgraph Extraction -- Classifier Construction by Graph-Based Induction for Graph-Structured Data -- Clustering I -- Comparison of the Performance of Center-Based Clustering Algorithms -- Automatic Extraction of Clusters from Hierarchical Clustering Representations -- Text Mining -- Large Scale Unstructured Document Classification Using Unlabeled Data and Syntactic Information -- Extracting Shared Topics of Multiple Documents -- An Empirical Study on Dimensionality Optimization in Text Mining for Linguistic Knowledge Acquisition -- A Semi-supervised Algorithm for Pattern Discovery in Information Extraction from Textual Data -- Bio Mining -- Mining Patterns of Dyspepsia Symptoms Across Time Points Using Constraint Association Rules -- Predicting Protein Structural Class from Closed Protein Sequences -- Learning Rules to Extract Protein Interactions from Biomedical Text -- Predicting Protein Interactions in Human by Homologous Interactions in Yeast -- Web Mining -- Mining the Customer’s Up-To-Moment Preferences for E-commerce Recommendation -- A Graph-Based Optimization Algorithm for Website Topology Using Interesting Association Rules -- A Markovian Approach for Web User Profiling and Clustering -- Extracting User Interests from Bookmarks on the Web -- Stream Mining II -- Mining Frequent Instances on Workflows -- Real Time Video Data Mining for Surveillance Video Streams -- Distinguishing Causal and Acausal Temporal Relations -- Bayesian Networks -- Online Bayes Point Machines -- Exploiting Hierarchical Domain Values for Bayesian Learning -- A New Restricted Bayesian Network Classifier -- Clustering II -- AGRID: An Efficient Algorithm for Clustering Large High-Dimensional Datasets -- Multi-level Clustering and Reasoning about Its Clusters Using Region Connection Calculus -- An Efficient Cell-Based Clustering Method for Handling Large, High-Dimensional Data -- Association Rules I -- Enhancing SWF for Incremental Association Mining by Itemset Maintenance -- Reducing Rule Covers with Deterministic Error Bounds -- Evolutionary Approach for Mining Association Rules on Dynamic Databases -- Semi-structured Data Mining -- Position Coded Pre-order Linked WAP-Tree for Web Log Sequential Pattern Mining -- An Integrated System of Mining HTML Texts and Filtering Structured Documents -- A New Sequential Mining Approach to XML Document Similarity Computation -- Classification I -- Optimization of Fuzzy Rules for Classification Using Genetic Algorithm -- Fast Pattern Selection for Support Vector Classifiers -- Averaged Boosting: A Noise-Robust Ensemble Method -- Improving Performance of Decision Tree Algorithms with Multi-edited Nearest Neighbor Rule -- Data Analysis -- HOT: Hypergraph-Based Outlier Test for Categorical Data -- A Method for Aggregating Partitions, Applications in K.D.D. -- Efficiently Computing Iceberg Cubes with Complex Constraints through Bounding -- Extraction of Tag Tree Patterns with Contractible Variables from Irregular Semistructured Data -- Association Rules II -- Step-by-Step Regression: A More Efficient Alternative for Polynomial Multiple Linear Regression in Stream Cube -- Progressive Weighted Miner: An Efficient Method for Time-Constraint Mining -- Mining Open Source Software (OSS) Data Using Association Rules Network -- Parallel FP-Growth on PC Cluster -- Feature Selection -- Active Feature Selection Using Classes -- Electricity Based External Similarity of Categorical Attributes -- Weighted Proportional k-Interval Discretization for Naive-Bayes Classifiers -- Dealing with Relative Similarity in Clustering: An Indiscernibility Based Approach -- Stream Mining III -- Considering Correlation between Variables to Improve Spatiotemporal Forecasting -- Correlation Analysis of Spatial Time Series Datasets: A Filter-and-Refine Approach -- When to Update the Sequential Patterns of Stream Data? -- Clustering III -- A New Clustering Algorithm for Transaction Data via Caucus -- DBRS: A Density-Based Spatial Clustering Method with Random Sampling -- Optimized Clustering for Anomaly Intrusion Detection -- Classification II -- Finding Frequent Subgraphs from Graph Structured Data with Geometric Information and Its Application to Lossless Compression -- Upgrading ILP Rules to First-Order Bayesian Networks -- A Clustering Validity Assessment Index. |
| Altri titoli varianti | PAKDD '03 |
| Record Nr. | UNISA-996466357703316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16-18, 2001. Proceedings / / edited by David Cheung, Graham J. Williams, Qing Li
| Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16-18, 2001. Proceedings / / edited by David Cheung, Graham J. Williams, Qing Li |
| Edizione | [1st ed. 2001.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2001 |
| Descrizione fisica | 1 online resource (XVII, 599 p.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data structures (Computer science)
Artificial intelligence Information storage and retrieval Information technology Business—Data processing Application software Mathematical statistics Data Structures and Information Theory Artificial Intelligence Information Storage and Retrieval IT in Business Information Systems Applications (incl. Internet) Probability and Statistics in Computer Science |
| ISBN | 3-540-45357-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Keynote Presentations -- Incompleteness in Data Mining -- Mining E-Commerce Data: The Good, the Bad, and the Ugly -- Seamless Integration of Data Mining with DBMS and Applications -- Web Mining -- Applying Pattern Mining to Web Information Extraction -- Empirical Study of Recommender Systems Using Linear Classifiers -- iJADE eMiner - A Web-Based Mining Agent Based on Intelligent Java Agent Development Environment (iJADE) on Internet Shopping -- A Characterized Rating Recommend System -- Discovery of Frequent Tree Structured Patterns in Semistructured Web Documents -- Text Mining -- Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification -- Predictive Self-Organizing Networks for Text Categorization -- Meta-learning Models for Automatic Textual Document Categorization -- Efficient Algorithms for Concept Space Construction -- Topic Detection, Tracking, and Trend Analysis Using Self-Organizing Neural Networks -- Automatic Hypertext Construction through a Text Mining Approach by Self-Organizing Maps -- Applications and Tools -- Semantic Expectation-Based Causation Knowledge Extraction: A Study on Hong Kong Stock Movement Analysis -- A Toolbox Approach to Flexible and Efficient Data Mining -- Determining Progression in Glaucoma Using Visual Fields -- Seabreeze Prediction Using Bayesian Networks -- Semi-supervised Learning in Medical Image Database -- On Application of Rough Data Mining Methods to Automatic Construction of Student Models -- Concept Hierarchies -- Concept Approximation in Concept Lattice -- Generating Concept Hierarchies/Networks: Mining Additional Semantics in Relational Data -- Representing Large Concept Hierarchies Using Lattice Data Structure -- Feature Selection -- Feature Selection for Temporal Health Records -- Boosting the Performance of Nearest Neighbour Methods with Feature Selection -- Feature Selection for Meta-learning -- Interestingness -- Efficient Mining of Niches and Set Routines -- Evaluation of Interestingness Measures for Ranking Discovered Knowledge -- Peculiarity Oriented Mining and Its Application for Knowledge Discovery in Amino-Acid Data -- Sequence Mining -- Mining Sequence Patterns from Wind Tunnel Experimental Data for Flight Control -- Scalable Hierarchical Clustering Method for Sequences of Categorical Values -- FFS - An I/O-Efficient Algorithm for Mining Frequent Sequences -- Sequential Index Structure for Content-Based Retrieval -- Spatial and Temporal Mining -- The S 2-Tree: An Index Structure for Subsequence Matching of Spatial Objects -- Temporal Data Mining Using Hidden Markov-Local Polynomial Models -- Patterns Discovery Based on Time-Series Decomposition -- Criteria on Proximity Graphs for Boundary Extraction and Spatial Clustering -- Micro Similarity Queries in Time Series Database -- Association Mining -- Mining Optimal Class Association Rule Set -- Generating Frequent Patterns with the Frequent Pattern List -- User-Defined Association Mining -- Direct and Incremental Computing of Maximal Covering Rules -- Towards Efficient Data Re-mining (DRM) -- Data Allocation Algorithm for Parallel Association Rule Discovery -- Classification and Rule Induction -- Direct Domain Knowledge Inclusion in the PA3 Rule Induction Algorithm -- Hierarchical Classification of Documents with Error Control -- An Efficient Data Compression Approach to the Classification Task -- Combining the Strength of Pattern Frequency and Distance for Classification -- A Scalable Algorithm for Rule Post-pruning of Large Decision Trees -- Optimizing the Induction of Alternating Decision Trees -- Building Behaviour Knowledge Space to Make Classification Decision -- Clustering -- Efficient Hierarchical Clustering Algorithms Using Partially Overlapping Partitions -- A Rough Set-Based Clustering Method with Modification of Equivalence Relations -- Importance of Individual Variables in the k-Means Algorithm -- A Hybrid Approach to Clustering in Very Large Databases -- Advanced Topics and New Methods -- A Similarity Indexing Method for the Data Warehousing - Bit-Wise Indexing Method -- Rule Reduction over Numerical Attributes in Decision Trees Using Multilayer Perceptron -- Knowledge Acquisition from Both Human Expert and Data -- Neighborhood Dependencies for Prediction -- Learning Bayesian Networks with Hidden Variables Using the Combination of EM and Evolutionary Algorithms -- Interactive Construction of Decision Trees -- An Improved Learning Algorithm for Augmented Naive Bayes -- Generalised RBF Networks Trained Using an IBL Algorithm for Mining Symbolic Data. |
| Record Nr. | UNINA-9910143597403321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16-18, 2001. Proceedings / / edited by David Cheung, Graham J. Williams, Qing Li
| Advances in Knowledge Discovery and Data Mining [[electronic resource] ] : 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16-18, 2001. Proceedings / / edited by David Cheung, Graham J. Williams, Qing Li |
| Edizione | [1st ed. 2001.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2001 |
| Descrizione fisica | 1 online resource (XVII, 599 p.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Data structures (Computer science)
Artificial intelligence Information storage and retrieval Information technology Business—Data processing Application software Mathematical statistics Data Structures and Information Theory Artificial Intelligence Information Storage and Retrieval IT in Business Information Systems Applications (incl. Internet) Probability and Statistics in Computer Science |
| ISBN | 3-540-45357-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Keynote Presentations -- Incompleteness in Data Mining -- Mining E-Commerce Data: The Good, the Bad, and the Ugly -- Seamless Integration of Data Mining with DBMS and Applications -- Web Mining -- Applying Pattern Mining to Web Information Extraction -- Empirical Study of Recommender Systems Using Linear Classifiers -- iJADE eMiner - A Web-Based Mining Agent Based on Intelligent Java Agent Development Environment (iJADE) on Internet Shopping -- A Characterized Rating Recommend System -- Discovery of Frequent Tree Structured Patterns in Semistructured Web Documents -- Text Mining -- Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification -- Predictive Self-Organizing Networks for Text Categorization -- Meta-learning Models for Automatic Textual Document Categorization -- Efficient Algorithms for Concept Space Construction -- Topic Detection, Tracking, and Trend Analysis Using Self-Organizing Neural Networks -- Automatic Hypertext Construction through a Text Mining Approach by Self-Organizing Maps -- Applications and Tools -- Semantic Expectation-Based Causation Knowledge Extraction: A Study on Hong Kong Stock Movement Analysis -- A Toolbox Approach to Flexible and Efficient Data Mining -- Determining Progression in Glaucoma Using Visual Fields -- Seabreeze Prediction Using Bayesian Networks -- Semi-supervised Learning in Medical Image Database -- On Application of Rough Data Mining Methods to Automatic Construction of Student Models -- Concept Hierarchies -- Concept Approximation in Concept Lattice -- Generating Concept Hierarchies/Networks: Mining Additional Semantics in Relational Data -- Representing Large Concept Hierarchies Using Lattice Data Structure -- Feature Selection -- Feature Selection for Temporal Health Records -- Boosting the Performance of Nearest Neighbour Methods with Feature Selection -- Feature Selection for Meta-learning -- Interestingness -- Efficient Mining of Niches and Set Routines -- Evaluation of Interestingness Measures for Ranking Discovered Knowledge -- Peculiarity Oriented Mining and Its Application for Knowledge Discovery in Amino-Acid Data -- Sequence Mining -- Mining Sequence Patterns from Wind Tunnel Experimental Data for Flight Control -- Scalable Hierarchical Clustering Method for Sequences of Categorical Values -- FFS - An I/O-Efficient Algorithm for Mining Frequent Sequences -- Sequential Index Structure for Content-Based Retrieval -- Spatial and Temporal Mining -- The S 2-Tree: An Index Structure for Subsequence Matching of Spatial Objects -- Temporal Data Mining Using Hidden Markov-Local Polynomial Models -- Patterns Discovery Based on Time-Series Decomposition -- Criteria on Proximity Graphs for Boundary Extraction and Spatial Clustering -- Micro Similarity Queries in Time Series Database -- Association Mining -- Mining Optimal Class Association Rule Set -- Generating Frequent Patterns with the Frequent Pattern List -- User-Defined Association Mining -- Direct and Incremental Computing of Maximal Covering Rules -- Towards Efficient Data Re-mining (DRM) -- Data Allocation Algorithm for Parallel Association Rule Discovery -- Classification and Rule Induction -- Direct Domain Knowledge Inclusion in the PA3 Rule Induction Algorithm -- Hierarchical Classification of Documents with Error Control -- An Efficient Data Compression Approach to the Classification Task -- Combining the Strength of Pattern Frequency and Distance for Classification -- A Scalable Algorithm for Rule Post-pruning of Large Decision Trees -- Optimizing the Induction of Alternating Decision Trees -- Building Behaviour Knowledge Space to Make Classification Decision -- Clustering -- Efficient Hierarchical Clustering Algorithms Using Partially Overlapping Partitions -- A Rough Set-Based Clustering Method with Modification of Equivalence Relations -- Importance of Individual Variables in the k-Means Algorithm -- A Hybrid Approach to Clustering in Very Large Databases -- Advanced Topics and New Methods -- A Similarity Indexing Method for the Data Warehousing - Bit-Wise Indexing Method -- Rule Reduction over Numerical Attributes in Decision Trees Using Multilayer Perceptron -- Knowledge Acquisition from Both Human Expert and Data -- Neighborhood Dependencies for Prediction -- Learning Bayesian Networks with Hidden Variables Using the Combination of EM and Evolutionary Algorithms -- Interactive Construction of Decision Trees -- An Improved Learning Algorithm for Augmented Naive Bayes -- Generalised RBF Networks Trained Using an IBL Algorithm for Mining Symbolic Data. |
| Record Nr. | UNISA-996465775103316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in Time Series Analysis and Forecasting [[electronic resource] ] : Selected Contributions from ITISE 2016 / / edited by Ignacio Rojas, Héctor Pomares, Olga Valenzuela
| Advances in Time Series Analysis and Forecasting [[electronic resource] ] : Selected Contributions from ITISE 2016 / / edited by Ignacio Rojas, Héctor Pomares, Olga Valenzuela |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (412 pages) |
| Disciplina | 519.55 |
| Collana | Contributions to Statistics |
| Soggetto topico |
Statistics
Econometrics Computer science—Mathematics Mathematical statistics Probabilities Statistics in Business, Management, Economics, Finance, Insurance Probability and Statistics in Computer Science Probability Theory Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences |
| ISBN | 3-319-55789-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- Part I: Analysis of Irregularly Sampled Time Series: Techniques, Algorithms and Case Studies -- Scientific Contributions -- Part II: Multi-scale Analysis of Univariate and Multivariate Time Series -- Scientific Contributions -- Part III: Linear and Non-linear Time Series Models -- Scientific Contributions -- Part IV: Advanced Time Series Forecasting Methods -- Scientific Contributions -- Part V: Applications in Time Series Analysis and Forecasting -- Scientific Contributions -- Author Index. |
| Record Nr. | UNINA-9910254304803321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Adversarial and Uncertain Reasoning for Adaptive Cyber Defense [[electronic resource] ] : Control- and Game-Theoretic Approaches to Cyber Security / / edited by Sushil Jajodia, George Cybenko, Peng Liu, Cliff Wang, Michael Wellman
| Adversarial and Uncertain Reasoning for Adaptive Cyber Defense [[electronic resource] ] : Control- and Game-Theoretic Approaches to Cyber Security / / edited by Sushil Jajodia, George Cybenko, Peng Liu, Cliff Wang, Michael Wellman |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (VII, 263 p. 120 illus., 45 illus. in color.) |
| Disciplina | 005.8 |
| Collana | Security and Cryptology |
| Soggetto topico |
Computer crimes
Computer organization Computers Mathematical statistics Computer Crime Computer Systems Organization and Communication Networks Computing Milieux Information Systems and Communication Service Probability and Statistics in Computer Science |
| ISBN | 3-030-30719-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Overview of Control and Game Theory in Adaptive Cyber-Defenses -- Control Theoretic Approaches to Cyber-Security -- Game-Theoretic Approaches to Cyber-Security: Issues and Challenges and Results -- Reinforcement Learning for Adaptive Cyber Defense against Zero-day Attacks -- Moving Target Defense Quantification -- Empirical Game-Theoretic Methods for Adaptive Cyber-Defense -- MTD Techniques for Memory Protection against Zero-Day Attacks -- Adaptive Cyber Defenses for Botnet Detection and Mitigation -- Optimizing Alert Data Management Processes at a Cyber Security Operations Center -- Online and Scalable Adaptive Cyber Defense. |
| Record Nr. | UNISA-996466448203316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Adversarial and Uncertain Reasoning for Adaptive Cyber Defense [[electronic resource] ] : Control- and Game-Theoretic Approaches to Cyber Security / / edited by Sushil Jajodia, George Cybenko, Peng Liu, Cliff Wang, Michael Wellman
| Adversarial and Uncertain Reasoning for Adaptive Cyber Defense [[electronic resource] ] : Control- and Game-Theoretic Approaches to Cyber Security / / edited by Sushil Jajodia, George Cybenko, Peng Liu, Cliff Wang, Michael Wellman |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (VII, 263 p. 120 illus., 45 illus. in color.) |
| Disciplina | 005.8 |
| Collana | Security and Cryptology |
| Soggetto topico |
Computer crimes
Computer organization Computers Mathematical statistics Computer Crime Computer Systems Organization and Communication Networks Computing Milieux Information Systems and Communication Service Probability and Statistics in Computer Science |
| ISBN | 3-030-30719-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Overview of Control and Game Theory in Adaptive Cyber-Defenses -- Control Theoretic Approaches to Cyber-Security -- Game-Theoretic Approaches to Cyber-Security: Issues and Challenges and Results -- Reinforcement Learning for Adaptive Cyber Defense against Zero-day Attacks -- Moving Target Defense Quantification -- Empirical Game-Theoretic Methods for Adaptive Cyber-Defense -- MTD Techniques for Memory Protection against Zero-Day Attacks -- Adaptive Cyber Defenses for Botnet Detection and Mitigation -- Optimizing Alert Data Management Processes at a Cyber Security Operations Center -- Online and Scalable Adaptive Cyber Defense. |
| Record Nr. | UNINA-9910349301503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Algorithmic advances in Riemannian geometry and applications [[electronic resource] ] : for machine learning, computer vision, statistics, and optimization / / edited by Hà Quang Minh, Vittorio Murino
| Algorithmic advances in Riemannian geometry and applications [[electronic resource] ] : for machine learning, computer vision, statistics, and optimization / / edited by Hà Quang Minh, Vittorio Murino |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XIV, 208 p. 55 illus., 51 illus. in color.) |
| Disciplina | 516.373 |
| Collana | Advances in Computer Vision and Pattern Recognition |
| Soggetto topico |
Pattern recognition
Computational intelligence Statistics Computer science—Mathematics Computer mathematics Artificial intelligence Mathematical statistics Pattern Recognition Computational Intelligence Statistics and Computing/Statistics Programs Mathematical Applications in Computer Science Artificial Intelligence Probability and Statistics in Computer Science |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Bayesian Statistical Shape Analysis on the Manifold of Diffeomorphisms -- Sampling Constrained Probability Distributions using Spherical Augmentation -- Geometric Optimization in Machine Learning -- Positive Definite Matrices: Data Representation and Applications to Computer Vision -- From Covariance Matrices to Covariance Operators: Data Representation from Finite to Infinite-Dimensional Settings -- Dictionary Learning on Grassmann Manifolds -- Regression on Lie Groups and its Application to Affine Motion Tracking -- An Elastic Riemannian Framework for Shape Analysis of Curves and Tree-Like Structures. |
| Record Nr. | UNINA-9910255014703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
