Using Evidence of Mixed Populations to Select Variables for Clustering Very High-Dimensional Data

Journal of the American Statistical Association ◽

10.1198/jasa.2010.tm09404 ◽

2010 ◽

Vol 105 (490) ◽

pp. 798-809 ◽

Author(s):

Yao-ban Chan ◽

Peter Hall

Keyword(s):

High Dimensional Data ◽

High Dimensional ◽

Mixed Populations ◽

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Feature selection algorithms for very high dimensional data and mixed data

10.32657/10356/41404 ◽

2008 ◽

Author(s):

Wen Yin Tang

Keyword(s):

Feature Selection ◽

High Dimensional Data ◽

High Dimensional ◽

Selection Algorithms ◽

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Extensions to Quantile Regression Forests for Very High-Dimensional Data

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

10.1007/978-3-319-06605-9_21 ◽

2014 ◽

pp. 247-258 ◽

Author(s):

Nguyen Thanh Tung ◽

Joshua Zhexue Huang ◽

Imran Khan ◽

Mark Junjie Li ◽

Graham Williams

Keyword(s):

Quantile Regression ◽

High Dimensional Data ◽

High Dimensional ◽

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Optimal properties of centroid-based classifiers for very high-dimensional data

The Annals of Statistics ◽

10.1214/09-aos736 ◽

2010 ◽

Vol 38 (2) ◽

pp. 1071-1093 ◽

Author(s):

Peter Hall ◽

Tung Pham

Keyword(s):

High Dimensional Data ◽

High Dimensional ◽

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Statistical analysis of very high-dimensional data sets of hierarchically structured binary variables with missing data: An application to marine corps readiness evaluations

Naval Research Logistics Quarterly ◽

10.1002/nav.3800320310 ◽

1985 ◽

Vol 32 (3) ◽

pp. 467-490 ◽

Author(s):

S. Zacks ◽

W. H. Marlow ◽

S. S. Brier

Keyword(s):

Statistical Analysis ◽

Missing Data ◽

High Dimensional Data ◽

Marine Corps ◽

High Dimensional ◽

Data Sets ◽

Binary Variables ◽

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A fast farthest neighbor search algorithm for very high dimensional data

2016 19th International Conference on Computer and Information Technology (ICCIT) ◽

10.1109/iccitechn.2016.7860222 ◽

2016 ◽

Author(s):

Shafin Rahman ◽

Mrigank Rochan

Keyword(s):

Search Algorithm ◽

High Dimensional Data ◽

High Dimensional ◽

Neighbor Search ◽

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Mining Frequent Patterns from Very High Dimensional Data: A Top-Down Row Enumeration Approach

Proceedings of the 2006 SIAM International Conference on Data Mining ◽

10.1137/1.9781611972764.25 ◽

2006 ◽

Author(s):

Hongyan Liu ◽

Jiawei Han ◽

Dong Xin ◽

Zheng Shao

Keyword(s):

High Dimensional Data ◽

High Dimensional ◽

Frequent Patterns ◽

Top Down ◽

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The Remarkable Simplicity of Very High Dimensional Data: Application of Model-Based Clustering

Journal of Classification ◽

10.1007/s00357-009-9037-9 ◽

2009 ◽

Vol 26 (3) ◽

pp. 249-277 ◽

Author(s):

Fionn Murtagh

Keyword(s):

High Dimensional Data ◽

High Dimensional ◽

Model Based Clustering ◽

Model Based ◽

Data Application ◽

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Top-down mining of frequent closed patterns from very high dimensional data

Information Sciences ◽

10.1016/j.ins.2008.11.033 ◽

2009 ◽

Vol 179 (7) ◽

pp. 899-924 ◽

Author(s):

Hongyan Liu ◽

Xiaoyu Wang ◽

Jun He ◽

Jiawei Han ◽

Dong Xin ◽

...

Keyword(s):

High Dimensional Data ◽

High Dimensional ◽

Top Down ◽

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Comparison of Classification Methods for Very High-Dimensional Data in Sparse Random Projection Representation

Proceedings in Adaptation, Learning and Optimization - Proceedings of ELM 2018 ◽

10.1007/978-3-030-23307-5_3 ◽

2019 ◽

pp. 17-26

Author(s):

Anton Akusok ◽

Emil Eirola

Keyword(s):

High Dimensional Data ◽

Random Projection ◽

High Dimensional ◽

Classification Methods ◽

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Lossy reduction for very high dimensional data

Proceedings 18th International Conference on Data Engineering ◽

10.1109/icde.2002.994783 ◽

2003 ◽

Author(s):

C. Jermaine ◽

E. Omiecinski

Keyword(s):

High Dimensional Data ◽

High Dimensional ◽

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