On the public citation graph
Optimal variable weighting for ultrametric and additive tree clustering
0 signed · 97 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/bf01593790openalex-snapshot
Restart procedures for the conjugate gradient method
inferredcites →https://doi.org/10.1007/bf02293884openalex-snapshot
A Least Squares Algorithm for Fitting Additive Trees to Proximity Data
inferredcites →https://doi.org/10.1007/bf02293907openalex-snapshot
An Examination of the Effect of Six Types of Error Perturbation on Fifteen Clustering Algorithms
inferredcites →https://doi.org/10.1007/bf02296969openalex-snapshot
Spatial, Non-Spatial and Hybrid Models for Scaling
inferredcites →https://doi.org/10.1007/bf02306021openalex-snapshot
The Estimation of Ultrametric and Path Length Trees from Rectangular Proximity Data
inferredcites →https://doi.org/10.1016/0022-2496(78)90029-9openalex-snapshot
Free trees and bidirectional trees as representations of psychological distance
inferredcites →https://doi.org/10.1016/0167-8655(84)90036-9openalex-snapshot
A least squares algorithm for fitting an ultrametric tree to a dissimilarity matrix
inferredcites →https://doi.org/10.1016/c2013-0-06161-0openalex-snapshot
Cluster Analysis for Applications
inferredcites →https://doi.org/10.1016/s0166-4115(08)62099-3openalex-snapshot
Tree Representations of Rectangular Proximity Matrices
inferredcites →https://doi.org/10.1017/s0021900200036378openalex-snapshot
Unrooted trees for numerical taxonomy
inferredcites →https://doi.org/10.1093/sysbio/19.1.58openalex-snapshot
Adaptive Hierarchical Clustering Schemes
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/0471264385.wei0207openalex-snapshot
Clustering and Classification Methods
inferred← citeshttps://doi.org/10.1002/9781118133880.hop202007openalex-snapshot
Clustering and Classification Methods
inferred← citeshttps://doi.org/10.1002/cne.22127openalex-snapshot
Retinal ganglion cells in the eastern newt Notophthalmus viridescens: Topography, morphology, and diversity
inferred← citeshttps://doi.org/10.1002/cne.23489openalex-snapshot
Retinal ganglion cells in the Pacific redfin, Tribolodon brandtii dybowski, 1872: Morphology and diversity
inferred← citeshttps://doi.org/10.1002/j.2333-8504.1994.tb01609.xopenalex-snapshot
VARIABLE SCREENING FOR CLUSTER ANALYSIS
inferred← citeshttps://doi.org/10.1002/j.2333-8504.1994.tb01619.xopenalex-snapshot
A PRELIMINARY STUDY OF THE EFFECTS OF WITHIN‐GROUP COVARIANCE STRUCTURE ON RECOVERY IN CLUSTER ANALYSIS
inferred← citeshttps://doi.org/10.1002/j.2333-8504.1995.tb01670.xopenalex-snapshot
A PRELIMINARY COMPARISON OF THE EFFECTIVENESS OF CLUSTER ANALYSIS WEIGHTING PROCEDURES FOR WITHIN‐GROUP COVARIANCE STRUCTURE
inferred← citeshttps://doi.org/10.1002/j.2333-8504.1999.tb01806.xopenalex-snapshot
THE EFFECTS OF WITHIN‐GROUP COVARIANCE STRUCTURE ON RECOVERY IN CLUSTER ANALYSIS: II. EXTENSION TO THE P‐DIMENSIONAL CASE
inferred← citeshttps://doi.org/10.1007/11875581_97openalex-snapshot
A Non-parametric Method for Data Clustering with Optimal Variable Weighting
inferred← citeshttps://doi.org/10.1007/978-1-4613-9290-3_53openalex-snapshot
Discussion by R. Gnanadesikan
inferred← citeshttps://doi.org/10.1007/978-3-319-17356-6_36openalex-snapshot
An Empirical Assessment of Two Univariate Screening Measures in Cluster Analysis
inferred← citeshttps://doi.org/10.1007/978-3-319-19090-7_35openalex-snapshot
A Clustering-Based Approach to Reduce Feature Redundancy
inferred← citeshttps://doi.org/10.1007/978-3-319-24211-8_13openalex-snapshot
Extending Kmeans-Type Algorithms by Integrating Intra-cluster Compactness and Inter-cluster Separation
inferred← citeshttps://doi.org/10.1007/978-3-319-26187-4_9openalex-snapshot
A Soft Subspace Clustering Method for Text Data Using a Probability Based Feature Weighting Scheme
inferred← citeshttps://doi.org/10.1007/978-3-642-50974-2_1openalex-snapshot
Hierarchical Clustering of Sampled Functions
inferred← citeshttps://doi.org/10.1007/978-3-642-79999-0_1openalex-snapshot
Advances in Cluster Analysis Relevant to Marketing Research
inferred← citeshttps://doi.org/10.1007/978-3-7908-2604-3_33openalex-snapshot
The Generic Subspace Clustering Model
inferred← citeshttps://doi.org/10.1007/bf01202271openalex-snapshot
Weighting and selection of variables for cluster analysis
inferred← citeshttps://doi.org/10.1007/bf01901677openalex-snapshot
OVWTRE: A program for optimal variable weighting for ultrametric and additive tree fitting
inferred← citeshttps://doi.org/10.1007/bf01908074openalex-snapshot
Optimal variable weighting for hierarchical clustering: An alternating least-squares algorithm
inferred← citeshttps://doi.org/10.1007/bf01908588openalex-snapshot
A validation study of a variable weighting algorithm for cluster analysis
inferred← citeshttps://doi.org/10.1007/bf01908720openalex-snapshot
A preliminary study of optimal variable weighting in k-means clustering
inferred← citeshttps://doi.org/10.1007/bf02294838openalex-snapshot
A Variable-Selection Heuristic for K-means Clustering
inferred← citeshttps://doi.org/10.1007/s00357-001-0018-xopenalex-snapshot
Optimal Variable Weighting for Ultrametric and Additive Trees and K-means Partitioning: Methods and Software
inferred← citeshttps://doi.org/10.1007/s00357-005-0015-6openalex-snapshot
OCLUS: An Analytic Method for Generating Clusters with Known Overlap
inferred← citeshttps://doi.org/10.1007/s00357-015-9167-1openalex-snapshot
Feature Relevance in Ward’s Hierarchical Clustering Using the L p Norm
inferred← citeshttps://doi.org/10.1007/s00357-016-9208-4openalex-snapshot
A Survey on Feature Weighting Based K-Means Algorithms
inferred← citeshttps://doi.org/10.1007/s10044-013-0341-yopenalex-snapshot
Double indices-induced FCM clustering and its integration with fuzzy subspace clustering
inferred← citeshttps://doi.org/10.1007/s11042-019-07974-7openalex-snapshot
View-collaborative fuzzy soft subspace clustering for automatic medical image segmentation
inferred← citeshttps://doi.org/10.1016/j.aei.2021.101435openalex-snapshot
Reassessment of climate zones for high-level pavement analysis using machine learning algorithms and NASA MERRA-2 data
inferred← citeshttps://doi.org/10.1016/j.asoc.2011.07.002openalex-snapshot
EEW-SC: Enhanced Entropy-Weighting Subspace Clustering for high dimensional gene expression data clustering analysis
inferred← citeshttps://doi.org/10.1016/j.csda.2008.03.002openalex-snapshot
Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm
inferred← citeshttps://doi.org/10.1016/j.ins.2016.01.101openalex-snapshot
A survey on soft subspace clustering
inferred← citeshttps://doi.org/10.1016/j.ins.2016.07.060openalex-snapshot
Investigating the role of the rating prediction task in granularity-based group recommender systems and big data scenarios
inferred← citeshttps://doi.org/10.1016/j.jweia.2022.105124openalex-snapshot
A zoning method for the extreme wind pressure coefficients of buildings based on weighted K-means clustering
inferred← citeshttps://doi.org/10.1016/j.knosys.2014.07.009openalex-snapshot
DSKmeans: A new kmeans-type approach to discriminative subspace clustering
inferred← citeshttps://doi.org/10.1016/j.neucom.2012.11.009openalex-snapshot
A weighting k-modes algorithm for subspace clustering of categorical data
inferred← citeshttps://doi.org/10.1016/j.patcog.2009.09.010openalex-snapshot
Enhanced soft subspace clustering integrating within-cluster and between-cluster information
inferred← citeshttps://doi.org/10.1016/j.patcog.2011.04.024openalex-snapshot
A novel attribute weighting algorithm for clustering high-dimensional categorical data
inferred← citeshttps://doi.org/10.1016/j.sapharm.2007.02.001openalex-snapshot
The uses and benefits of cluster analysis in pharmacy research
inferred← citeshttps://doi.org/10.1017/s0952523811000320openalex-snapshot
A multivariate approach to structural heterogeneity of retinal ganglion cells
inferred← citeshttps://doi.org/10.1037/1082-989x.9.4.510openalex-snapshot
Clustering Binary Data in the Presence of Masking Variables.
inferred← citeshttps://doi.org/10.1109/access.2019.2924491openalex-snapshot
Sequential Minimax Search for Multi-Layer Gene Grouping
inferred← citeshttps://doi.org/10.1109/eit.2005.1627020openalex-snapshot
Mining Gene Microarray Data with Adaptive Feature Scaling
inferred← citeshttps://doi.org/10.1109/fuzz-ieee.2012.6251344openalex-snapshot
Double indices induced FCM clustering and its integration with fuzzy subspace clustering
inferred← citeshttps://doi.org/10.1109/ieec.2009.17openalex-snapshot
Weighted k-Means Algorithm Based Text Clustering
inferred← citeshttps://doi.org/10.1109/tfuzz.2010.2047947openalex-snapshot
Robust Relief-Feature Weighting, Margin Maximization, and Fuzzy Optimization
inferred← citeshttps://doi.org/10.1109/tkde.2007.1048openalex-snapshot
An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
inferred← citeshttps://doi.org/10.1109/tkde.2011.262openalex-snapshot
TW-k-means: Automated two-level variable weighting clustering algorithm for multiview data
inferred← citeshttps://doi.org/10.1109/tnnls.2013.2293795openalex-snapshot
Extensions of Kmeans-Type Algorithms: A New Clustering Framework by Integrating Intracluster Compactness and Intercluster Separation
inferred← citeshttps://doi.org/10.1109/tpami.2005.95openalex-snapshot
Automated variable weighting in k-means type clustering
inferred← citeshttps://doi.org/10.1111/j.0081-1750.2003.00134.xopenalex-snapshot
10. Settings in Social Networks: A Measurement Model
inferred← citeshttps://doi.org/10.1111/j.1467-9868.2004.02059.xopenalex-snapshot
Clustering Objects on Subsets of Attributes (with Discussion)
inferred← citeshttps://doi.org/10.1142/s021812660900571xopenalex-snapshot
CENTROID NEURAL NETWORK WITH WEIGHTED FEATURES
inferred← citeshttps://doi.org/10.1146/annurev.ps.43.020192.001125openalex-snapshot
Combinatorial Data Analysis
inferred← citeshttps://doi.org/10.1155/jbb.2005.215openalex-snapshot
Finding Groups in Gene Expression Data
inferred← citeshttps://doi.org/10.1175/1520-0442(1993)006<2103:czotcu>2.0.co;2openalex-snapshot
Climate Zones of the Conterminous United States Defined Using Cluster Analysis
inferred← citeshttps://doi.org/10.1177/002224379903600408openalex-snapshot
HINoV: A New Model to Improve Market Segment Definition by Identifying Noisy Variables
inferred← citeshttps://doi.org/10.1177/076737019000500104openalex-snapshot
Analyse structurelle et analyse typologique: Illustration d'une démarche complémentaire
inferred← citeshttps://doi.org/10.1177/1476127012457983openalex-snapshot
Firm performance and aspiration levels as determinants of a firm’s strategic repositioning within strategic group structures
inferred← citeshttps://doi.org/10.1186/1471-2148-8-86openalex-snapshot
General functions to transform associate data to host data, and their use in phylogenetic inference from sequences with intra-individual variability
inferred← citeshttps://doi.org/10.1207/s15327906mbr3002_6openalex-snapshot
The Effects of Within-group Covariance Structure on Recovery in Cluster Analysis: I. The Bivariate Case
inferred← citeshttps://doi.org/10.1207/s15327906mbr3003_5openalex-snapshot
Univariate Screening Measures for Cluster Analysis
inferred← citeshttps://doi.org/10.1207/s15327906mbr3502_5openalex-snapshot
Validating Cluster Analysis: Consistent Replication and Symmetry
inferred← citeshttps://doi.org/10.1214/16-aos1423aopenalex-snapshot
Discussion of “Influential features PCA for high dimensional clustering”
inferred← citeshttps://doi.org/10.1300/j142v04n01_07openalex-snapshot
An Investigation of the Efficacy of Univariate Screening Measures in Defining Market Structure
inferred← citeshttps://doi.org/10.1348/000711005x48266openalex-snapshot
K‐means clustering: A half‐century synthesis
inferred← citeshttps://doi.org/10.1504/ijdmb.2008.019092openalex-snapshot
Message Passing Clustering (MPC): a knowledge-based framework for clustering under biological constraints
inferred← citeshttps://doi.org/10.18535/ijetst/v3i02.04openalex-snapshot
A Survey Paper on Modified Approach for Kmeans Algorithm
inferred← citeshttps://doi.org/10.18778/7525-695-6openalex-snapshot
Metody selekcji zmiennych w analizie skupień. Nowe procedury
inferred← citeshttps://doi.org/10.26180/4621684openalex-snapshot
Classification methods for time series
inferred← citeshttps://doi.org/10.26686/wgtn.17019287openalex-snapshot
Improving Clustering Methods By Exploiting Richness Of Text Data
inferred← citeshttps://doi.org/10.2991/icsnce-18.2018.2openalex-snapshot
Improved K-means Algorithm Based on Optimizing Initial Cluster Centers and Its Application
inferred← citeshttps://doi.org/10.3233/ida-163222openalex-snapshot
A co-evolutionary framework for adaptive multidimensional data clustering
inferred← citeshttps://doi.org/10.3233/jifs-16157openalex-snapshot
A weighted k -modes clustering using new weighting method based on within-cluster and between-cluster impurity measures
inferred← citeshttps://doi.org/10.3390/e21070683openalex-snapshot
An Entropy Regularization k-Means Algorithm with a New Measure of between-Cluster Distance in Subspace Clustering
inferred← citeshttps://doi.org/10.3390/info10060208openalex-snapshot
Latent Feature Group Learning for High-Dimensional Data Clustering
inferred← citeshttps://doi.org/10.5120/15291-3942openalex-snapshot
An Algorithm for Automated View Reduction in Weighted Clustering of Multiview Data
inferred← citeshttps://doi.org/10.5351/kjas.2012.25.3.471openalex-snapshot
A Variable Selection Procedure for K-Means Clustering
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.