Feature Weighting in k-Means Clustering

By Dharmendra S. Modha, W. Scott Spangler. Machine-mediated learning, 2003

399 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/29c02531a44357ef556659e5a8678800c2034bb5

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Citations in the corpus

An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data (2007, background): “Variable weighting for clustering is an important research topic in statistics and data mining [13], [30], [31], [32], [15], [21].”
Automated variable weighting in k-means type clustering (2005, methodology): “The weights are optimized from the entire weight space rather than from a limited number of candidates as in Modha and Spangler's approach [13].”
Simultaneous feature selection and clustering using mixture models (2004, methodology): “In [43], weights are assigned to different groups of features for k-means clustering based on a score related to the Fisher discriminant.”
A review of feature selection methods with applications (2015, neutral): “Feature weighting Kmeans multivariate, statistical clustering [14]”
Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering (2012, methodology): “To accommodate the linear weights, Modha and Spangler [16] drastically change the K-Means criterion by dividing its extension, ''the within-cluster dispersion'' over an index expressing ''the between-cluster dispersion .''”
A Survey of Clustering Data Mining Techniques (2006, background): “Modha & Spangler [2002] suggested a very interesting approach for attribute scaling that pursues the objective of clustering in each attribute subset by computing weights (a simplex) that minimize the product of intra-cluster to inter-cluster ratios for the attribute subset projections (called gener”
Feature Selection for Clustering: A Review (2018, methodology): “In addition, [47] proposed feature weighting k-means clustering using generalized Fisher ratio that minimizes the ratio of the average of within-cluster distortion over the average between-cluster distortion.”
Clustering with Bregman Divergences (2005, methodology): “For example, [ 19 ] presents the convexkmeans clustering for distortion measures that are always non-negative and convex in the second argument, using the notion of a generalized centroid.”
A Divisive Information-Theoretic Feature Clustering Algorithm for Text Classification (2003, methodology): “Our divisive algorithm bears some resemblance to the k-means or Lloyd-Max algorithm, which usually uses squared Euclidean distances (also see Gray and Neuhoff, 1998, Berkhin and Becher, 2002, Vaithyanathan and Dom, 1999, Modha and Spangler, 2002, to appear).”