A Novel Stability Based Feature Selection Framework for k-means Clustering

Dimitrios Mavroeidis; Elena Marchiori

doi:10.1007/978-3-642-23783-6_27

ScienceGate Book Chapters

BOOK-CHAPTER

A Novel Stability Based Feature Selection Framework for k-means Clustering

Dimitrios Mavroeidis Elena Marchiori

Year: 2011 Lecture notes in computer science Pages: 421-436 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-642-23783-6_27

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Feature selection Cluster analysis Selection (genetic algorithm) Stability (learning theory) Artificial intelligence Pattern recognition (psychology) Data mining Machine learning

Metrics

Cited By

0.51

FWCI (Field Weighted Citation Impact)

Refs

0.64

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Advanced Clustering Algorithms Research

Physical Sciences → Computer Science → Artificial Intelligence

Gene expression and cancer classification

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

A Novel Stability Based Feature Selection Framework for k-means Clustering

Metrics

Citation History

Topics

Related Documents

A Novel Multilayer Data Clustering Framework based on Feature Selection and Modified K-Means Algorithm

Feature Selection Algorithm Based on K-means Clustering

Flexible Subspace Clustering: A Joint Feature Selection and K-Means Clustering Framework

Deterministic Feature Selection for K-Means Clustering

Novel K-Means Clustering-based Undersampling and Feature Selection for Drug Discovery Applications