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Advances in Knowledge Discovery and Data Mining 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16-18, 2001. Proceedings

Aus der Reihe Lecture Notes in Artificial Intelligence

97,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

04.04.2001

Herausgeber

David Cheung + weitere

Verlag

Springer Berlin

Seitenzahl

599

Maße (L/B/H)

23,5/15,5/3,4 cm

Gewicht

925 g

Auflage

2001

Sprache

Englisch

ISBN

978-3-540-41910-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

04.04.2001

Herausgeber

Verlag

Springer Berlin

Seitenzahl

599

Maße (L/B/H)

23,5/15,5/3,4 cm

Gewicht

925 g

Auflage

2001

Sprache

Englisch

ISBN

978-3-540-41910-5

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: [email protected]

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  • Produktbild: Advances in Knowledge Discovery and Data Mining
  • Produktbild: Advances in Knowledge Discovery and Data Mining
  • 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.