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Advances in Knowledge Discovery and Data Mining

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Table of Contents

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    Book Overview
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    Chapter 1 Network Data Mining and Analysis: The $$ \mathcal{N}\mathcal{E}\mathcal{M}\mathcal{E}\mathcal{S}\mathcal{I}\mathcal{S} $$ Project
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    Chapter 2 Privacy Preserving Data Mining: Challenges and Opportunities
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    Chapter 3 A Case for Analytical Customer Relationship Management
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    Chapter 4 On Data Clustering Analysis: Scalability, Constraints, and Validation
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    Chapter 5 Discovering Numeric Association Rules via Evolutionary Algorithm
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    Chapter 6 Efficient Rule Retrieval and Postponed Restrict Operations for Association Rule Mining
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    Chapter 7 Association Rule Mining on Remotely Sensed Images Using P-trees
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    Chapter 8 On the Efficiency of Association-Rule Mining Algorithms
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    Chapter 9 A Function-Based Classifier Learning Scheme Using Genetic Programming
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    Chapter 10 SNNB: A Selective Neighborhood Based Naïve Bayes for Lazy Learning
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    Chapter 11 A Method to Boost Naïve Bayesian Classifiers
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    Chapter 12 Toward Bayesian Classifiers with Accurate Probabilities
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    Chapter 13 Pruning Redundant Association Rules Using Maximum Entropy Principle
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    Chapter 14 A Confidence-Lift Support Specification for Interesting Associations Mining
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    Chapter 15 Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators
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    Chapter 16 Mining Interesting Association Rules: A Data Mining Language
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    Chapter 17 The Lorenz Dominance Order as a Measure of Interestingness in KDD
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    Chapter 18 Efficient Algorithms for Incremental Update of Frequent Sequences
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    Chapter 19 DELISP: Efficient Discovery of Generalized Sequential Patterns by Delimited Pattern-Growth Technology
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    Chapter 20 Self-Similarity for Data Mining and Predictive Modeling A Case Study for Network Data
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    Chapter 21 A New Mechanism of Mining Network Behavior
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    Chapter 22 M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining
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    Chapter 23 An Incremental Hierarchical Data Clustering Algorithm Based on Gravity Theory
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    Chapter 24 Adding Personality to Information Clustering
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    Chapter 25 Clustering Large Categorical Data
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    Chapter 26 WebFrame: In Pursuit of Computationally and Cognitively Efficient Web Mining
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    Chapter 27 Naviz:Website Navigational Behavior Visualizer
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    Chapter 28 Optimal Algorithms for Finding User Access Sessions from Very Large Web Logs
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    Chapter 29 Automatic Information Extraction for Multiple Singular Web Pages
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    Chapter 30 An Improved Approach for the Discovery of Causal Models via MML
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    Chapter 31 SETM*-MaxK: An Efficient SET-Based Approach to Find the Largest Itemset
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    Chapter 32 Discovery of Ordinal Association Rules
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    Chapter 33 Value Added Association Rules
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    Chapter 34 Top Down FP-Growth for Association Rule Mining
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    Chapter 35 Discovery of Frequent Tag Tree Patterns in Semistructured Web Documents
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    Chapter 36 Extracting Characteristic Structures among Words in Semistructured Documents
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    Chapter 37 An Efficient Algorithm for Incremental Update of Concept Spaces
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    Chapter 38 Efficient Constraint-Based Exploratory Mining on Large Data Cubes
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    Chapter 39 Efficient Utilization of Materialized Views in a Data Warehouse
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    Chapter 40 Mining Interesting Rules in Meningitis Data by Cooperatively Using GDT-RS and RSBR
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    Chapter 41 Evaluation of Techniques for Classifying Biological Sequences
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    Chapter 42 Efficiently Mining Gene Expression Data via Integrated Clustering and Validation Techniques
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    Chapter 43 Adaptive Generalized Estimation Equation with Bayes Classifier for the Job Assignment Problem
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    Chapter 44 GEC: An Evolutionary Approach for Evolving Classifiers
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    Chapter 45 An Efficient Single-Scan Algorithm for Mining Essential Jumping Emerging Patterns for Classification
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    Chapter 46 A Method to Boost Support Vector Machines
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    Chapter 47 Distribution Discovery: Local Analysis of Temporal Rules
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    Chapter 48 News Sensitive Stock Trend Prediction
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    Chapter 49 User Profiling for Intrusion Detection Using Dynamic and Static Behavioral Models
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    Chapter 50 Incremental Extraction of Keyterms for Classifying Multilingual Documents in the Web
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    Chapter 51 k-nearest Neighbor Classification on Spatial Data Streams Using P-trees
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    Chapter 52 Interactive Construction of Classification Rules
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    Chapter 53 Enhancing Effectiveness of Outlier Detections for Low Density Patterns
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    Chapter 54 Cluster-Based Algorithms for Dealing with Missing Values
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    Chapter 55 Extracting Causation Knowledge from Natural Language Texts
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    Chapter 56 Mining Relationship Graphs for Effective Business Objectives
Overall attention for this book and its chapters
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Mentioned by

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Citations

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Title
Advances in Knowledge Discovery and Data Mining
Published by
Springer, Berlin, Heidelberg, January 2002
DOI 10.1007/3-540-47887-6
ISBNs
978-3-54-043704-8, 978-3-54-047887-4
Editors

Ming-Syan Chen, Philip S. Yu, Bing Liu

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 4%
Brazil 1 1%
India 1 1%
United States 1 1%
Iran, Islamic Republic of 1 1%
Hungary 1 1%
Unknown 62 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 26%
Student > Master 17 24%
Student > Bachelor 6 9%
Professor > Associate Professor 5 7%
Student > Postgraduate 5 7%
Other 19 27%
Readers by discipline Count As %
Computer Science 38 54%
Business, Management and Accounting 9 13%
Unspecified 6 9%
Engineering 5 7%
Physics and Astronomy 3 4%
Other 9 13%