Principles of Data Mining and Knowledge Discovery - Lecture Notes in Computer Science
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Published By Springer Berlin Heidelberg

9783540410669, 9783540453727

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Learning Dynamic Bayesian Belief Networks Using Conditional Phase-Type Distributions

Principles of Data Mining and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/3-540-45372-5_60 ◽

2007 ◽

pp. 516-523 ◽

Author(s):

Adele Marshall ◽

Sally McClean ◽

Mary Shapcott1 ◽

Peter Millard

Keyword(s):

Bayesian Belief Networks ◽

Belief Networks ◽

Phase Type Distributions ◽

Learning Dynamic ◽

Conditional Phase

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Combining Multiple Models with Meta Decision Trees

Principles of Data Mining and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/3-540-45372-5_6 ◽

2000 ◽

pp. 54-64 ◽

Author(s):

Ljupčo Todorovski ◽

Sašo Džeroski

Keyword(s):

Decision Trees ◽

Multiple Models

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Confirmation Rule Sets

Principles of Data Mining and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/3-540-45372-5_4 ◽

2000 ◽

pp. 34-43 ◽

Author(s):

Dragan Gamberger ◽

Nada Lavrač

Keyword(s):

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Lightweight Document Clustering

Principles of Data Mining and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/3-540-45372-5_82 ◽

2000 ◽

pp. 665-672 ◽

Author(s):

Sholom M. Weiss ◽

Brian F. White ◽

Chidanand V. Apte

Keyword(s):

Document Clustering

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Empirical Evaluation of Feature Subset Selection Based on a Real-World Data Set

Principles of Data Mining and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/3-540-45372-5_68 ◽

2000 ◽

pp. 575-580 ◽

Author(s):

Petra Perner ◽

Chid Apte

Keyword(s):

Empirical Evaluation ◽

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Real World Data ◽

Data Set ◽

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Discovery of Characteristic Subgraph Patterns Using Relative Indexing and the Cascade Model

Principles of Data Mining and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/3-540-45372-5_65 ◽

2000 ◽

pp. 550-557 ◽

Author(s):

Takashi Okada ◽

Mayumi Oyama

Keyword(s):

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Transparency and Predictive Power

Principles of Data Mining and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/3-540-45372-5_66 ◽

2000 ◽

pp. 558-565

Author(s):

Gerhard Paass ◽

Jörg Kindermann

Keyword(s):

Predictive Power

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Mining Generalized Multiple-level Association Rules

Principles of Data Mining and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/3-540-45372-5_84 ◽

2000 ◽

pp. 679-684 ◽

Author(s):

Yen Show-Jane

Keyword(s):

Association Rules ◽

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Fast Feature Selection using Partial Correlation for Multi-valued Attributes

Principles of Data Mining and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/3-540-45372-5_22 ◽

2000 ◽

pp. 221-231 ◽

Author(s):

S. Lallich ◽

R. Rakotomalala

Keyword(s):

Feature Selection ◽

Partial Correlation

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Accurate Recasting of Parameter Estimation Algorithms Using Sufficient Statistics for Efficient Parallel Speed-Up

Principles of Data Mining and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/3-540-45372-5_24 ◽

2000 ◽

pp. 243-254 ◽

Author(s):

Bin Zhang ◽

Meichun Hsu ◽

George Forman

Keyword(s):

Parameter Estimation ◽

Sufficient Statistics ◽

Estimation Algorithms ◽

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