| 000 | 07562nam a22006135i 4500 | ||
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| 001 | 978-3-540-48912-2 | ||
| 003 | DE-He213 | ||
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| 008 | 121227s1999 gw | s |||| 0|eng d | ||
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_a9783540489122 _9978-3-540-48912-2 |
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_a10.1007/3-540-48912-6 _2doi |
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| 050 | 4 | _aQ334-342 | |
| 050 | 4 | _aTA347.A78 | |
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_aUYQ _2bicssc |
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_aCOM004000 _2bisacsh |
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_a006.3 _223 |
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_aMethodologies for Knowledge Discovery and Data Mining _h[electronic resource] : _bThird Pacific-Asia Conference, PAKDD'99, Beijing, China, April 26-28, 1999, Proceedings / _cedited by Ning Zhong, Lizhu Zhou. |
| 250 | _a1st ed. 1999. | ||
| 264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c1999. |
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| 300 |
_aXVI, 540 p. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v1574 |
|
| 505 | 0 | _aInvited Talks -- KDD as an Enterprise IT Tool: Reality and Agenda -- Computer Assisted Discovery of First Principle Equations from Numeric Data -- Emerging KDD Technology -- Data Mining — a Rough Set Perspective -- Data Mining Techniques for Associations, Clustering and Classification -- Data Mining: Granular Computing Approach -- Rule Extraction from Prediction Models -- Association Rules -- Mining Association Rules on Related Numeric Attributes -- LGen — A Lattice-Based Candidate Set Generation Algorithm for I/O Efficient Association Rule Mining -- Extending the Applicability of Association Rules -- An Efficient Approach for Incremental Association Rule Mining -- Association Rules in Incomplete Databases -- Parallel SQL Based Association Rule Mining on Large Scale PC Cluster: Performance Comparison with Directly Coded C Implementation -- H-Rule Mining in Heterogeneous Databases -- An Improved Definition of Multidimensional Inter-transaction Association Rule -- Incremental Discovering Association Rules: A Concept Lattice Approach -- Feature Selection and Generation -- Induction as Pre-processing -- Stochastic Attribute Selection Committees with Multiple Boosting: Learning More Accurate and More Stable Classifier Committees -- On Information-Theoretic Measures of Attribute Importance -- A Technique of Dynamic Feature Selection Using the Feature Group Mutual Information -- A Data Pre-processing Method Using Association Rules of Attributes for Improving Decision Tree -- Mining in Semi, Un-structured Data -- An Algorithm for Constrained Association Rule Mining in Semi-structured Data -- Incremental Mining of Schema for Semistructured Data -- Discovering Structure from Document Databases -- Combining Forecasts from Multiple Textual Data Sources -- Domain Knowledge Extracting in a Chinese NaturalLanguage Interface to Databases: NChiql -- Interestingness, Surprisingness, and Exceptions -- Evolutionary Hot Spots Data Mining -- Efficient Search of Reliable Exceptions -- Heuristics for Ranking the Interestingness of Discovered Knowledge -- Rough Sets, Fuzzy Logic, and Neural Networks -- Automated Discovery of Plausible Rules Based on Rough Sets and Rough Inclusion -- Discernibility System in Rough Sets -- Automatic Labeling of Self-Organizing Maps: Making a Treasure-Map Reveal Its Secrets -- Neural Network Based Classifiers for a Vast Amount of Data -- Accuracy Tuning on Combinatorial Neural Model -- A Situated Information Articulation Neural Network: VSF Network -- Neural Method for Detection of Complex Patterns in Databases -- Preserve Discovered Linguistic Patterns Valid in Volatility Data Environment -- An Induction Algorithm Based on Fuzzy Logic Programming -- Rule Discovery in Databases with Missing Values Based on Rough Set Model -- Sustainability Knowledge Mining from Human Development Database -- Induction, Classification, and Clustering -- Characterization of Default Knowledge in Ripple Down Rules Method -- Improving the Performance of Boosting for Naive Bayesian Classification -- Convex Hulls in Concept Induction -- Mining Classification Knowledge Based on Cloud Models -- Robust Clusterin of Large Geo-referenced Data Sets -- A Fast Algorithm for Density-Based Clustering in Large Database -- A Lazy Model-Based Algorithm for On-Line Classification -- An Efficient Space-Partitioning Based Algorithm for the K-Means Clustering -- A Fast Clustering Process for Outliers and Remainder Clusters -- Optimising the Distance Metric in the Nearest Neighbour Algorithm on a Real-World Patient Classification Problem -- Classifying Unseen Cases with Many Missing Values -- Study of a Mixed SimilarityMeasure for Classification and Clustering -- Visualization -- Visually Aided Exploration of Interesting Association Rules -- DVIZ: A System for Visualizing Data Mining -- Causal Model and Graph-Based Methods -- A Minimal Causal Model Learner -- Efficient Graph-Based Algorithm for Discovering and Maintaining Knowledge in Large Databases -- Basket Analysis for Graph Structured Data -- The Evolution of Causal Models: A Comparison of Bayesian Metrics and Structure Priors -- KD-FGS: A Knowledge Discovery System from Graph Data Using Formal Graph System -- Agent-Based, and Distributed Data Mining -- Probing Knowledge in Distributed Data Mining -- Discovery of Equations and the Shared Operational Semantics in Distributed Autonomous Databases -- The Data-Mining and the Technology of Agents to Fight the Illicit Electronic Messages -- Knowledge Discovery in SportsFinder: An Agent to Extract Sports Results from the Web -- Event Mining with Event Processing Networks -- Advanced Topics and New Methodologies -- An Analysis of Quantitative Measures Associated with Rules -- A Strong Relevant Logic Model of Epistemic Processes in Scientific Discovery -- Discovering Conceptual Differences among Different People via Diverse Structures -- Ordered Estimation of Missing Values -- Prediction Rule Discovery Based on Dynamic Bias Selection -- Discretization of Continuous Attributes for Learning Classification Rules -- BRRA: A Based Relevant Rectangles Algorithm for Mining Relationships in Databases -- Mining Functional Dependency Rule of Relational Database -- Time-Series Prediction with Cloud Models in DMKD. | |
| 650 | 0 | _aArtificial intelligence. | |
| 650 | 0 | _aDatabase management. | |
| 650 | 0 | _aInformation storage and retrieval systems. | |
| 650 | 0 | _aApplication software. | |
| 650 | 0 | _aBusiness information services. | |
| 650 | 1 | 4 | _aArtificial Intelligence. |
| 650 | 2 | 4 | _aDatabase Management. |
| 650 | 2 | 4 | _aInformation Storage and Retrieval. |
| 650 | 2 | 4 | _aComputer and Information Systems Applications. |
| 650 | 2 | 4 | _aIT in Business. |
| 700 | 1 |
_aZhong, Ning. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 700 | 1 |
_aZhou, Lizhu. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783540658665 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783662171127 |
| 830 | 0 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v1574 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/3-540-48912-6 |
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