Advances in Knowledge Discovery and Data Mining
Springer Berlin (Verlag)
978-3-540-04760-5 (ISBN)
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.
| Erscheint lt. Verlag | 16.4.2003 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | XVIII, 614 p. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 880 g |
| Themenwelt | Informatik ► Theorie / Studium ► Algorithmen |
| Schlagworte | Algorithmic Learning • algorithms • Association Rule Mining • Calculus • classification • Clustering • Data Analysis • Database • Database Mining • Data Mining • Dom • E-Commerce • Hardcover, Softcover / Informatik, EDV/Informatik • HC/Informatik, EDV/Informatik • information extraction • Learning classifier systems • Optimization • service-oriented computing • Statistical Learning • Text Mining • Text Summarization • Web mining • Workflow |
| ISBN-10 | 3-540-04760-3 / 3540047603 |
| ISBN-13 | 978-3-540-04760-5 / 9783540047605 |
| Zustand | Neuware |
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