Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm

DOI10.1016/j.csda.2008.03.002zbMath1452.62471OpenAlexW1989946714MaRDI QIDQ1023820

Chieh-Yuan Tsai, Chuang-Cheng Chiu

Publication date: 16 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2008.03.002

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (9)

A soft computing model based on asymmetric Gaussian mixtures and Bayesian inference ⋮ A survey on feature weighting based K-means algorithms ⋮ Novel soft subspace clustering with multi-objective evolutionary approach for high-dimensional data ⋮ Community detection with structural and attribute similarities ⋮ Clustering large attributed information networks: an efficient incremental computing approach ⋮ Adaptive initialization method based on spatial local information for \(k\)-means algorithm ⋮ A survey on soft subspace clustering ⋮ A feature group weighting method for subspace clustering of high-dimensional data ⋮ Kernel-based hard clustering methods in the feature space with automatic variable weighting

Uses Software

• J-MEANS
• UCI-ml
• OVWTRE
• COSA

Cites Work

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• Optimal variable weighting for ultrametric and additive trees and \(K\)-means partitioning: Methods and software.
• Validity index for crisp and fuzzy clusters
• An optimization algorithm for clustering using weighted dissimilarity measures
• Weighting and selection of variables for cluster analysis
• A variable-selection heuristic for K-means clustering
• Clustering Objects on Subsets of Attributes (with Discussion)
• Variable Selection for Model-Based Clustering
• A Novel Fuzzy C-Means Clustering Algorithm
• J-MEANS: A new local search heuristic for minimum sum of squares clustering

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