Journal ArticleDOI
Variable selection in clustering
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TLDR
A forward selection procedure for identifying the subset of variables is proposed and studied in the context of complete linkage hierarchical clustering, and can be applied to other clustering methods, too.Abstract:
Standard clustering algorithms can completely fail to identify clear cluster structure if that structure is confined to a subset of the variables. A forward selection procedure for identifying the subset is proposed and studied in the context of complete linkage hierarchical clustering. The basic approach can be applied to other clustering methods, too.read more
Citations
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Journal ArticleDOI
K‐means clustering: A half‐century synthesis
TL;DR: This paper synthesizes the results, methodology, and research conducted concerning the K-means clustering method over the last fifty years, leading to a unifying treatment of K-Means and some of its extensions.
Journal ArticleDOI
Automated variable weighting in k-means type clustering
TL;DR: A new step is introduced to the k-means clustering process to iteratively update variable weights based on the current partition of data and a formula for weight calculation is proposed, and the convergency theorem of the new clustered process is given.
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Subspace clustering
TL;DR: The problems motivating subspace clustering are sketched, different definitions and usages of subspaces for clusteringare described, and exemplary algorithmic solutions are discussed.
Journal ArticleDOI
Clustering objects on subsets of attributes (with discussion)
TL;DR: A new procedure is proposed for clustering attribute value data that encourages those algorithms to detect automatically subgroups of objects that preferentially cluster on subsets of the attribute variables rather than on all of them simultaneously.
References
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Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Journal ArticleDOI
Direct Clustering of a Data Matrix
TL;DR: This article presents a model, and a technique, for clustering cases and variables simultaneously and the principal advantage in this approach is the direct interpretation of the clusters on the data.
Book
Methods for Statistical Data Analysis of Multivariate Observations
TL;DR: In this paper, the authors present an assessment of specific aspects of multivariate statistical models, including reduction of dimensionality, reduction of dependence, and clustering of multidimensional dependencies.
Journal ArticleDOI
A study of standardization of variables in cluster analysis
TL;DR: The present simulation study examined the standardization problem and found that those approaches which standardize by division by the range of the variable gave consistently superior recovery of the underlying cluster structure.