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2002, ISBN: 9783540478874

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Bibliographische Daten des bestpassenden Buches

Details zum Buch

Detailangaben zum Buch - Advances in Knowledge Discovery and Data Mining


EAN (ISBN-13): 9783540478874
Erscheinungsjahr: 2002
Herausgeber: Springer Berlin Heidelberg

Buch in der Datenbank seit 2017-05-10T02:41:27+02:00 (Berlin)
Detailseite zuletzt geändert am 2023-08-14T12:00:50+02:00 (Berlin)
ISBN/EAN: 9783540478874

ISBN - alternative Schreibweisen:
978-3-540-47887-4
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: ming cheng, bing liu
Titel des Buches: data mining, knowledge discovery, pacific discovery


Daten vom Verlag:

Autor/in: Ming-Syan Cheng; Philip S. Yu; Bing Liu
Titel: Lecture Notes in Artificial Intelligence; Lecture Notes in Computer Science; Advances in Knowledge Discovery and Data Mining - 6th Pacific-Asia Conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002. Proceedings
Verlag: Springer; Springer Berlin
570 Seiten
Erscheinungsjahr: 2003-08-01
Berlin; Heidelberg; DE
Sprache: Englisch
96,29 € (DE)
99,00 € (AT)
118,00 CHF (CH)
Available
XIV, 570 p.

EA; E107; eBook; Nonbooks, PBS / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; classification; clustering; data mining; data warehouse; knowledge; knowledge discovery; web mining; C; Artificial Intelligence; Data Structures and Information Theory; Database Management; Probability and Statistics in Computer Science; Statistics and Computing; Computers and Society; Computer Science; Algorithmen und Datenstrukturen; Informationstheorie; Datenbanken; Mathematik für Informatiker; Wahrscheinlichkeitsrechnung und Statistik; Mathematische und statistische Software; Digital- und Informationstechnologien: soziale und ethische Aspekte; BC

Industrial Papers (Invited).- Network Data Mining and Analysis: The Project.- Privacy Preserving Data Mining: Challenges and Opportunities.- Survey Papers (Invited).- A Case for Analytical Customer Relationship Management.- On Data Clustering Analysis: Scalability, Constraints, and Validation.- Association Rules (I).- Discovering Numeric Association Rules via Evolutionary Algorithm.- Efficient Rule Retrieval and Postponed Restrict Operations for Association Rule Mining.- Association Rule Mining on Remotely Sensed Images Using P-trees.- On the Efficiency of Association-Rule Mining Algorithms.- Classification (I).- A Function-Based Classifier Learning Scheme Using Genetic Programming.- SNNB: A Selective Neighborhood Based Naïve Bayes for Lazy Learning.- A Method to Boost Naïve Bayesian Classifiers.- Toward Bayesian Classifiers with Accurate Probabilities.- Interestingness.- Pruning Redundant Association Rules Using Maximum Entropy Principle.- A Confidence-Lift Support Specification for Interesting Associations Mining.- Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators.- Mining Interesting Association Rules: A Data Mining Language.- The Lorenz Dominance Order as a Measure of Interestingness in KDD.- Sequence Mining.- Efficient Algorithms for Incremental Update of Frequent Sequences.- DELISP: Efficient Discovery of Generalized Sequential Patterns by Delimited Pattern-Growth Technology.- Self-Similarity for Data Mining and Predictive Modeling A Case Study for Network Data.- A New Mechanism of Mining Network Behavior.- Clustering.- M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining.- An Incremental Hierarchical Data Clustering Algorithm Based on Gravity Theory.- Adding Personality toInformation Clustering.- Clustering Large Categorical Data.- Web Mining.- WebFrame: In Pursuit of Computationally and Cognitively Efficient Web Mining.- Naviz:Website Navigational Behavior Visualizer.- Optimal Algorithms for Finding User Access Sessions from Very Large Web Logs.- Automatic Information Extraction for Multiple Singular Web Pages.- Association Rules (II).- An Improved Approach for the Discovery of Causal Models via MML.- SETM*-MaxK: An Efficient SET-Based Approach to Find the Largest Itemset.- Discovery of Ordinal Association Rules.- Value Added Association Rules.- Top Down FP-Growth for Association Rule Mining.- Semi-structure & Concept Mining.- Discovery of Frequent Tag Tree Patterns in Semistructured Web Documents.- Extracting Characteristic Structures among Words in Semistructured Documents.- An Efficient Algorithm for Incremental Update of Concept Spaces.- Data Warehouse and Data Cube.- Efficient Constraint-Based Exploratory Mining on Large Data Cubes.- Efficient Utilization of Materialized Views in a Data Warehouse.- Bio-Data Mining.- Mining Interesting Rules in Meningitis Data by Cooperatively Using GDT-RS and RSBR.- Evaluation of Techniques for Classifying Biological Sequences.- Efficiently Mining Gene Expression Data via Integrated Clustering and Validation Techniques.- Classification (II).- Adaptive Generalized Estimation Equation with Bayes Classifier for the Job Assignment Problem.- GEC: An Evolutionary Approach for Evolving Classifiers.- An Efficient Single-Scan Algorithm for Mining Essential Jumping Emerging Patterns for Classification.- A Method to Boost Support Vector Machines.- Temporal Mining.- Distribution Discovery: Local Analysis of Temporal Rules.- News Sensitive Stock Trend Prediction.- User Profiling for Intrusion Detection Using Dynamic and Static Behavioral Models.- Classification (III).- Incremental Extraction of Keyterms for Classifying Multilingual Documents in the Web.- k-nearest Neighbor Classification on Spatial Data Streams Using P-trees.- Interactive Construction of Classification Rules.- Outliers, Missing Data, and Causation.- Enhancing Effectiveness of Outlier Detections for Low Density Patterns.- Cluster-Based Algorithms for Dealing with Missing Values.- Extracting Causation Knowledge from Natural Language Texts.- Mining Relationship Graphs for Effective Business Objectives.
Includes supplementary material: sn.pub/extras

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9780262560979 Fayyad, U: Advances in Knowledge Discovery & Data Mining (American Association for Artificial Intelligence) (Fayyad, Usama M., Gregory Piatetsky-Shapiro und Padhraic Smyth)


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