Methodologies for Knowledge Discovery and Data Mining (Record no. 188973)
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control field | 978-3-540-48912-2 |
003 - CONTROL NUMBER IDENTIFIER | |
control field | DE-He213 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20240423132539.0 |
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fixed length control field | 121227s1999 gw | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 9783540489122 |
-- | 978-3-540-48912-2 |
024 7# - OTHER STANDARD IDENTIFIER | |
Standard number or code | 10.1007/3-540-48912-6 |
Source of number or code | doi |
050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
Classification number | Q334-342 |
050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
Classification number | TA347.A78 |
072 #7 - SUBJECT CATEGORY CODE | |
Subject category code | UYQ |
Source | bicssc |
072 #7 - SUBJECT CATEGORY CODE | |
Subject category code | COM004000 |
Source | bisacsh |
072 #7 - SUBJECT CATEGORY CODE | |
Subject category code | UYQ |
Source | thema |
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
Classification number | 006.3 |
Edition number | 23 |
245 10 - TITLE STATEMENT | |
Title | Methodologies for Knowledge Discovery and Data Mining |
Medium | [electronic resource] : |
Remainder of title | Third Pacific-Asia Conference, PAKDD'99, Beijing, China, April 26-28, 1999, Proceedings / |
Statement of responsibility, etc | edited by Ning Zhong, Lizhu Zhou. |
250 ## - EDITION STATEMENT | |
Edition statement | 1st ed. 1999. |
264 #1 - | |
-- | Berlin, Heidelberg : |
-- | Springer Berlin Heidelberg : |
-- | Imprint: Springer, |
-- | 1999. |
300 ## - PHYSICAL DESCRIPTION | |
Extent | XVI, 540 p. |
Other physical details | online resource. |
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-- | computer |
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-- | online resource |
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-- | text file |
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490 1# - SERIES STATEMENT | |
Series statement | Lecture Notes in Artificial Intelligence, |
International Standard Serial Number | 2945-9141 ; |
Volume number/sequential designation | 1574 |
505 0# - FORMATTED CONTENTS NOTE | |
Formatted contents note | Invited 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 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Artificial intelligence. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Database management. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Information storage and retrieval systems. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Application software. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Business information services. |
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Artificial Intelligence. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Database Management. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Information Storage and Retrieval. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Computer and Information Systems Applications. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | IT in Business. |
700 1# - ADDED ENTRY--PERSONAL NAME | |
Personal name | Zhong, Ning. |
Relator term | editor. |
Relator code | edt |
-- | http://id.loc.gov/vocabulary/relators/edt |
700 1# - ADDED ENTRY--PERSONAL NAME | |
Personal name | Zhou, Lizhu. |
Relator term | editor. |
Relator code | edt |
-- | http://id.loc.gov/vocabulary/relators/edt |
710 2# - ADDED ENTRY--CORPORATE NAME | |
Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
773 0# - HOST ITEM ENTRY | |
Title | Springer Nature eBook |
776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
Display text | Printed edition: |
International Standard Book Number | 9783540658665 |
776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
Display text | Printed edition: |
International Standard Book Number | 9783662171127 |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
Uniform title | Lecture Notes in Artificial Intelligence, |
-- | 2945-9141 ; |
Volume number/sequential designation | 1574 |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | <a href="https://doi.org/10.1007/3-540-48912-6">https://doi.org/10.1007/3-540-48912-6</a> |
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942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | eBooks-CSE-Springer |
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