Likelihood-Based Data Squashing: A Modeling Approach to Instance Construction

Authors:
David Madigan;Nandini Raghavan;William Dumouchel;Martha Nason;Christian Posse;Greg Ridgeway
Affiliations:
Rutgers University. madigan@stat.rutgers.edu;AT&T Labs—Research. raghavan@research.att.com;AT&T Labs—Research. dumouchel@research.att.com;Talaria, Inc. mnason@talariainc.com;Talaria, Inc. posse@talariainc.com;University of Washington. greg@stat.washington.edu
Venue:
Data Mining and Knowledge Discovery
Year:
2002

Citing 7
Cited 10

Empirical model-building and response surface

Empirical model-building and response surface
Instance-Based Learning Algorithms

Machine Learning
BIRCH: an efficient data clustering method for very large databases

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Strategic directions in storage I/O issues in large-scale computing

ACM Computing Surveys (CSUR) - Special ACM 50th-anniversary issue: strategic directions in computing research
Squashing flat files flatter

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A study of support vectors on model independent example selection

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A Survey of Methods for Scaling Up Inductive Algorithms

Data Mining and Knowledge Discovery

On Issues of Instance Selection

Data Mining and Knowledge Discovery
Data Squashing by Empirical Likelihood

Data Mining and Knowledge Discovery
Applications of sampling and fractional factorial designs to model-free data squashing

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Toward Integrating Feature Selection Algorithms for Classification and Clustering

IEEE Transactions on Knowledge and Data Engineering
Spiral removal of exceptional patients for mining chronic hepatitis data

New Generation Computing
Fast approximate spectral clustering

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast support vector machines for continuous data

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Fast density-weighted low-rank approximation spectral clustering

Data Mining and Knowledge Discovery
Multi-strategy instance selection in mining chronic hepatitis data

ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
On instance selection in audio based emotion recognition

ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Squashing is a lossy data compression technique that preserves statistical information. Specifically, squashing compresses a massive dataset to a much smaller one so that outputs from statistical analyses carried out on the smaller (squashed) dataset reproduce outputs from the same statistical analyses carried out on the original dataset. Likelihood-based data squashing (LDS) differs from a previously published squashing algorithm insofar as it uses a statistical model to squash the data. The results show that LDS provides excellent squashing performance even when the target statistical analysis departs from the model used to squash the data.