Discovery Science - Lecture Notes in Computer Science
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Published By Springer Berlin Heidelberg

9783642047466, 9783642047473

Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

A Sliding Window Algorithm for Relational Frequent Patterns Mining from Data Streams

Discovery Science - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04747-3_30 ◽

2009 ◽

pp. 385-392 ◽

Cited By ~ 8

Author(s):

Fabio Fumarola ◽

Anna Ciampi ◽

Annalisa Appice ◽

Donato Malerba

Keyword(s):

Data Streams ◽

Sliding Window ◽

Frequent Patterns

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Algorithm and Feature Selection for VegOut: A Vegetation Condition Prediction Tool

Discovery Science - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04747-3_11 ◽

2009 ◽

pp. 107-120

Author(s):

Sherri Harms ◽

Tsegaye Tadesse ◽

Brian Wardlow

Keyword(s):

Feature Selection ◽

Prediction Tool ◽

Vegetation Condition ◽

Selection For

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A Comparison of Community Detection Algorithms on Artificial Networks

Discovery Science - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04747-3_20 ◽

2009 ◽

pp. 242-256 ◽

Cited By ~ 31

Author(s):

Günce Keziban Orman ◽

Vincent Labatut

Keyword(s):

Community Detection ◽

Detection Algorithms

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CHRONICLE: A Two-Stage Density-Based Clustering Algorithm for Dynamic Networks

Discovery Science - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04747-3_14 ◽

2009 ◽

pp. 152-167 ◽

Cited By ~ 7

Author(s):

Min-Soo Kim ◽

Jiawei Han

Keyword(s):

Clustering Algorithm ◽

Dynamic Networks ◽

Two Stage ◽

Density Based Clustering

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A Dialectic Approach to Problem-Solving

Discovery Science - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04747-3_34 ◽

2009 ◽

pp. 417-424

Author(s):

Eric Martin ◽

Jean Sallantin

Keyword(s):

Problem Solving

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Computing a Comprehensible Model for Spam Filtering

Discovery Science - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04747-3_39 ◽

2009 ◽

pp. 457-464

Author(s):

Amparo Ruiz-Sepúlveda ◽

José L. Triviño-Rodriguez ◽

Rafael Morales-Bueno

Keyword(s):

Spam Filtering

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An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting

Discovery Science - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04747-3_6 ◽

2009 ◽

pp. 36-50 ◽

Cited By ~ 5

Author(s):

Annalisa Appice ◽

Michelangelo Ceci ◽

Donato Malerba

Keyword(s):

Learning Algorithm ◽

Iterative Learning

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C-DenStream: Using Domain Knowledge on a Data Stream

Discovery Science - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04747-3_23 ◽

2009 ◽

pp. 287-301 ◽

Cited By ~ 15

Author(s):

Carlos Ruiz ◽

Ernestina Menasalvas ◽

Myra Spiliopoulou

Keyword(s):

Data Stream ◽

Domain Knowledge

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An Empirical Comparison of Probability Estimation Techniques for Probabilistic Rules

Discovery Science - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04747-3_25 ◽

2009 ◽

pp. 317-331

Author(s):

Jan-Nikolas Sulzmann ◽

Johannes Fürnkranz

Keyword(s):

Probability Estimation ◽

Empirical Comparison ◽

Estimation Techniques

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Finding the k-Most Abnormal Subgraphs from a Single Graph

Discovery Science - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04747-3_37 ◽

2009 ◽

pp. 441-448

Author(s):

JianBin Wang ◽

Bin-Hui Chou ◽

Einoshin Suzuki

Keyword(s):

Single Graph

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Discovery Science - Lecture Notes in Computer ScienceLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer Berlin Heidelberg

A Sliding Window Algorithm for Relational Frequent Patterns Mining from Data Streams

Algorithm and Feature Selection for VegOut: A Vegetation Condition Prediction Tool

A Comparison of Community Detection Algorithms on Artificial Networks

CHRONICLE: A Two-Stage Density-Based Clustering Algorithm for Dynamic Networks

A Dialectic Approach to Problem-Solving

Computing a Comprehensible Model for Spam Filtering

An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting

C-DenStream: Using Domain Knowledge on a Data Stream

An Empirical Comparison of Probability Estimation Techniques for Probabilistic Rules

Finding the k-Most Abnormal Subgraphs from a Single Graph

Discovery Science - Lecture Notes in Computer Science
Latest Publications