Automated variable weighting in k-means type clustering

By Joshua Zhexue Huang, Michael Kwok-Po Ng, Hongqiang Rong, Zichen Li. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005

827 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/12d38ec302a9aab483b7c7ee0dbeab8192ab7fc8

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Citations in the corpus

Encyclopedia of Information Science and Technology, Third Edition (2014, background): “Thus, it is convenient to have an algorithm able to measure attributes under different weights (Huang et al., 2005).”
Valuation of large variable annuity portfolios under nested simulation: A functional data approach (2015, background): “The distance between two contracts x and y in X can be defined as (Huang, 1998; Huang et al., 2005): D(x, y) =  d1 ”
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].”
Recovering the number of clusters in data sets with noise features using feature rescaling factors (2015, methodology): “scaling factors for any p. The latter is not true for other feature weighting methods for K-Means based on a powered weight, such as Weighted K-Means and the attributes-weighting clustering algorithm [18, 5]. By using the distance (5) in the K-Means criterion (1), we obtain the MWKMeans criterion: W”
Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering (2012, methodology): “[5, 6, 7] advise adding a positive value, the average feature variance, to each Du in formula (5).”
A k-mean clustering algorithm for mixed numeric and categorical data (2007, methodology): “[34] proposed k prototype clustering method for handling mixed data.”