Advances in Knowledge Discovery and Data Mining : 7th Pacific-Asia Conference, PAKDD 2003. Seoul, Korea, April 30 - May 2, 2003, Proceedings - nouveau livre

EAN (ISBN-13): 9783540361756
Date de parution: 2003
Editeur: Springer Berlin Heidelberg

Livre dans la base de données depuis 2017-04-25T23:18:07+02:00 (Paris)
Page de détail modifiée en dernier sur 2024-01-16T17:33:17+01:00 (Paris)
ISBN/EAN: 9783540361756

ISBN - Autres types d'écriture:
978-3-540-36175-6
Autres types d'écriture et termes associés:
Auteur du livre: jon young
Titre du livre: data mining, knowledge discovery, pacific discovery, seoul

---

Données de l'éditeur

Auteur: Kyu-Young Whang; Jongwoo Jeon; Kyuseok Shim; Jaideep Srivatava
Titre: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Advances in Knowledge Discovery and Data Mining - 7th Pacific-Asia Conference, PAKDD 2003. Seoul, Korea, April 30 - May 2, 2003, Proceedings
Editeur: Springer; Springer Berlin
614 Pages
Date de parution: 2003-08-03
Berlin; Heidelberg; DE
Langue: Anglais
96,29 € (DE)
99,00 € (AT)
118,00 CHF (CH)
Available
XVIII, 614 p.

EA; E107; eBook; Nonbooks, PBS / Informatik, EDV/Informatik; Algorithmen und Datenstrukturen; Verstehen; DOM; E-Commerce; Service-oriented Computing; Workflow; algorithms; calculus; classification; clustering; data mining; database; information extraction; learning classifier systems; optimization; statistical learning; web mining; C; Data Structures and Information Theory; Artificial Intelligence; Probability and Statistics in Computer Science; Database Management; Information Storage and Retrieval; Computer and Information Systems Applications; Computer Science; Informationstheorie; Künstliche Intelligenz; Mathematik für Informatiker; Wahrscheinlichkeitsrechnung und Statistik; Datenbanken; Informationsrückgewinnung, Information Retrieval; Data Warehousing; Angewandte Informatik; BC

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.- OnlineBayes 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.
Includes supplementary material: sn.pub/extras