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Advances in knowledge discovery and data mining : 6th Pacific-Asia conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002 : proceedings / / Ming-Syan Chen; Philip S. Yu; Bing Liu
| Advances in knowledge discovery and data mining : 6th Pacific-Asia conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002 : proceedings / / Ming-Syan Chen; Philip S. Yu; Bing Liu |
| Edizione | [1st ed. 2002.] |
| Pubbl/distr/stampa | Berlin, Germany ; ; New York, New York : , : Springer, , [2002] |
| Descrizione fisica | 1 online resource (XIV, 570 p.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Database searching
Data mining Database management |
| ISBN | 3-540-47887-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Industrial Papers (Invited) -- Network Data Mining and Analysis: The Project -- Privacy Preserving Data Mining: Challenges and Opportunities -- Survey Papers (Invited) -- A Case for Analytical Customer Relationship Management -- On Data Clustering Analysis: Scalability, Constraints, and Validation -- Association Rules (I) -- Discovering Numeric Association Rules via Evolutionary Algorithm -- Efficient Rule Retrieval and Postponed Restrict Operations for Association Rule Mining -- Association Rule Mining on Remotely Sensed Images Using P-trees -- On the Efficiency of Association-Rule Mining Algorithms -- Classification (I) -- A Function-Based Classifier Learning Scheme Using Genetic Programming -- SNNB: A Selective Neighborhood Based Naïve Bayes for Lazy Learning -- A Method to Boost Naïve Bayesian Classifiers -- Toward Bayesian Classifiers with Accurate Probabilities -- Interestingness -- Pruning Redundant Association Rules Using Maximum Entropy Principle -- A Confidence-Lift Support Specification for Interesting Associations Mining -- Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators -- Mining Interesting Association Rules: A Data Mining Language -- The Lorenz Dominance Order as a Measure of Interestingness in KDD -- Sequence Mining -- Efficient Algorithms for Incremental Update of Frequent Sequences -- DELISP: Efficient Discovery of Generalized Sequential Patterns by Delimited Pattern-Growth Technology -- Self-Similarity for Data Mining and Predictive Modeling A Case Study for Network Data -- A New Mechanism of Mining Network Behavior -- Clustering -- M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining -- An Incremental Hierarchical Data Clustering Algorithm Based on Gravity Theory -- Adding Personality to Information Clustering -- Clustering Large Categorical Data -- Web Mining -- WebFrame: In Pursuit of Computationally and Cognitively Efficient Web Mining -- Naviz:Website Navigational Behavior Visualizer -- Optimal Algorithms for Finding User Access Sessions from Very Large Web Logs -- Automatic Information Extraction for Multiple Singular Web Pages -- Association Rules (II) -- An Improved Approach for the Discovery of Causal Models via MML -- SETM*-MaxK: An Efficient SET-Based Approach to Find the Largest Itemset -- Discovery of Ordinal Association Rules -- Value Added Association Rules -- Top Down FP-Growth for Association Rule Mining -- Semi-structure & Concept Mining -- Discovery of Frequent Tag Tree Patterns in Semistructured Web Documents -- Extracting Characteristic Structures among Words in Semistructured Documents -- An Efficient Algorithm for Incremental Update of Concept Spaces -- Data Warehouse and Data Cube -- Efficient Constraint-Based Exploratory Mining on Large Data Cubes -- Efficient Utilization of Materialized Views in a Data Warehouse -- Bio-Data Mining -- Mining Interesting Rules in Meningitis Data by Cooperatively Using GDT-RS and RSBR -- Evaluation of Techniques for Classifying Biological Sequences -- Efficiently Mining Gene Expression Data via Integrated Clustering and Validation Techniques -- Classification (II) -- Adaptive Generalized Estimation Equation with Bayes Classifier for the Job Assignment Problem -- GEC: An Evolutionary Approach for Evolving Classifiers -- An Efficient Single-Scan Algorithm for Mining Essential Jumping Emerging Patterns for Classification -- A Method to Boost Support Vector Machines -- Temporal Mining -- Distribution Discovery: Local Analysis of Temporal Rules -- News Sensitive Stock Trend Prediction -- User Profiling for Intrusion Detection Using Dynamic and Static Behavioral Models -- Classification (III) -- Incremental Extraction of Keyterms for Classifying Multilingual Documents in the Web -- k-nearest Neighbor Classification on Spatial Data Streams Using P-trees -- Interactive Construction of Classification Rules -- Outliers, Missing Data, and Causation -- Enhancing Effectiveness of Outlier Detections for Low Density Patterns -- Cluster-Based Algorithms for Dealing with Missing Values -- Extracting Causation Knowledge from Natural Language Texts -- Mining Relationship Graphs for Effective Business Objectives. |
| Record Nr. | UNISA-996465985203316 |
| Berlin, Germany ; ; New York, New York : , : Springer, , [2002] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in knowledge discovery and data mining : 6th Pacific-Asia conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002 : proceedings / / Ming-Syan Chen; Philip S. Yu; Bing Liu
| Advances in knowledge discovery and data mining : 6th Pacific-Asia conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002 : proceedings / / Ming-Syan Chen; Philip S. Yu; Bing Liu |
| Edizione | [1st ed. 2002.] |
| Pubbl/distr/stampa | Berlin, Germany ; ; New York, New York : , : Springer, , [2002] |
| Descrizione fisica | 1 online resource (XIV, 570 p.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Database searching
Data mining Database management |
| ISBN | 3-540-47887-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Industrial Papers (Invited) -- Network Data Mining and Analysis: The Project -- Privacy Preserving Data Mining: Challenges and Opportunities -- Survey Papers (Invited) -- A Case for Analytical Customer Relationship Management -- On Data Clustering Analysis: Scalability, Constraints, and Validation -- Association Rules (I) -- Discovering Numeric Association Rules via Evolutionary Algorithm -- Efficient Rule Retrieval and Postponed Restrict Operations for Association Rule Mining -- Association Rule Mining on Remotely Sensed Images Using P-trees -- On the Efficiency of Association-Rule Mining Algorithms -- Classification (I) -- A Function-Based Classifier Learning Scheme Using Genetic Programming -- SNNB: A Selective Neighborhood Based Naïve Bayes for Lazy Learning -- A Method to Boost Naïve Bayesian Classifiers -- Toward Bayesian Classifiers with Accurate Probabilities -- Interestingness -- Pruning Redundant Association Rules Using Maximum Entropy Principle -- A Confidence-Lift Support Specification for Interesting Associations Mining -- Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators -- Mining Interesting Association Rules: A Data Mining Language -- The Lorenz Dominance Order as a Measure of Interestingness in KDD -- Sequence Mining -- Efficient Algorithms for Incremental Update of Frequent Sequences -- DELISP: Efficient Discovery of Generalized Sequential Patterns by Delimited Pattern-Growth Technology -- Self-Similarity for Data Mining and Predictive Modeling A Case Study for Network Data -- A New Mechanism of Mining Network Behavior -- Clustering -- M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining -- An Incremental Hierarchical Data Clustering Algorithm Based on Gravity Theory -- Adding Personality to Information Clustering -- Clustering Large Categorical Data -- Web Mining -- WebFrame: In Pursuit of Computationally and Cognitively Efficient Web Mining -- Naviz:Website Navigational Behavior Visualizer -- Optimal Algorithms for Finding User Access Sessions from Very Large Web Logs -- Automatic Information Extraction for Multiple Singular Web Pages -- Association Rules (II) -- An Improved Approach for the Discovery of Causal Models via MML -- SETM*-MaxK: An Efficient SET-Based Approach to Find the Largest Itemset -- Discovery of Ordinal Association Rules -- Value Added Association Rules -- Top Down FP-Growth for Association Rule Mining -- Semi-structure & Concept Mining -- Discovery of Frequent Tag Tree Patterns in Semistructured Web Documents -- Extracting Characteristic Structures among Words in Semistructured Documents -- An Efficient Algorithm for Incremental Update of Concept Spaces -- Data Warehouse and Data Cube -- Efficient Constraint-Based Exploratory Mining on Large Data Cubes -- Efficient Utilization of Materialized Views in a Data Warehouse -- Bio-Data Mining -- Mining Interesting Rules in Meningitis Data by Cooperatively Using GDT-RS and RSBR -- Evaluation of Techniques for Classifying Biological Sequences -- Efficiently Mining Gene Expression Data via Integrated Clustering and Validation Techniques -- Classification (II) -- Adaptive Generalized Estimation Equation with Bayes Classifier for the Job Assignment Problem -- GEC: An Evolutionary Approach for Evolving Classifiers -- An Efficient Single-Scan Algorithm for Mining Essential Jumping Emerging Patterns for Classification -- A Method to Boost Support Vector Machines -- Temporal Mining -- Distribution Discovery: Local Analysis of Temporal Rules -- News Sensitive Stock Trend Prediction -- User Profiling for Intrusion Detection Using Dynamic and Static Behavioral Models -- Classification (III) -- Incremental Extraction of Keyterms for Classifying Multilingual Documents in the Web -- k-nearest Neighbor Classification on Spatial Data Streams Using P-trees -- Interactive Construction of Classification Rules -- Outliers, Missing Data, and Causation -- Enhancing Effectiveness of Outlier Detections for Low Density Patterns -- Cluster-Based Algorithms for Dealing with Missing Values -- Extracting Causation Knowledge from Natural Language Texts -- Mining Relationship Graphs for Effective Business Objectives. |
| Record Nr. | UNINA-9910143906403321 |
| Berlin, Germany ; ; New York, New York : , : Springer, , [2002] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Heterogeneous graph representation learning and applications / / Chuan Shi, Xiao Wang, Philip S. Yu
| Heterogeneous graph representation learning and applications / / Chuan Shi, Xiao Wang, Philip S. Yu |
| Autore | Shi Chuan |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (329 pages) |
| Disciplina | 511.5 |
| Collana | Artificial intelligence |
| Soggetto topico |
Graph theory
Machine learning |
| ISBN | 981-16-6166-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464535603316 |
Shi Chuan
|
||
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Heterogeneous graph representation learning and applications / / Chuan Shi, Xiao Wang, Philip S. Yu
| Heterogeneous graph representation learning and applications / / Chuan Shi, Xiao Wang, Philip S. Yu |
| Autore | Shi Chuan |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (329 pages) |
| Disciplina | 511.5 |
| Collana | Artificial intelligence |
| Soggetto topico |
Graph theory
Machine learning |
| ISBN |
981-16-6166-9
981-16-6165-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910743356503321 |
|
Shi Chuan
|
||
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Heterogeneous graph representation learning and applications / / Chuan Shi, Xiao Wang, Philip S. Yu
| Heterogeneous graph representation learning and applications / / Chuan Shi, Xiao Wang, Philip S. Yu |
| Autore | Shi Chuan |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (329 pages) |
| Disciplina | 511.5 |
| Collana | Artificial intelligence |
| Soggetto topico |
Graph theory
Machine learning |
| ISBN |
981-16-6166-9
981-16-6165-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996549371203316 |
|
Shi Chuan
|
||
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||