A feature group weighting method for subspace clustering of high-dimensional data

DOI10.1016/j.patcog.2011.06.004zbMath1225.68162OpenAlexW2044080809MaRDI QIDQ139002

Joshua Zhexue Huang, Xiaofei Xu, Yunming Ye, Xiaojun Chen, Joshua Zhexue Huang, Yunming Ye, Xiaofei Xu, Xiaojun Chen

Publication date: January 2012

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2011.06.004

zbMATH Keywords

data mining\(k\)-meanshigh-dimensional data analysisfeature weightingsubspace clustering

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items

A survey on feature weighting based K-means algorithms, Subspace clustering with automatic feature grouping, Distance metric learning for soft subspace clustering in composite kernel space, Novel soft subspace clustering with multi-objective evolutionary approach for high-dimensional data, A general framework for subspace detection in unordered multidimensional data, Enhanced soft subspace clustering through hybrid dissimilarity, A survey on soft subspace clustering, Unsupervised feature selection with ensemble learning, Enhanced balanced min cut, wskm

Uses Software

• UCI-ml
• COSA

Cites Work

• High-dimensional data clustering
• Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm
• Synthesized clustering: A method for amalgamating alternative clustering bases with differential weighting of variables
• Feature weighting in \(k\)-means clustering
• An optimization algorithm for clustering using weighted dissimilarity measures
• Enhanced soft subspace clustering integrating within-cluster and between-cluster information
• Clustering Objects on Subsets of Attributes (with Discussion)
• Model-based subspace clustering