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A comparative study of clustering ensemble algorithms

https://doi.org/10.1016/j.compeleceng.2018.05.005

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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$$\varvec{\textit{KDVEM}}$$ KDVEM : a $$k$$ k -degree anonymity with vertex and edge modification algorithm

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Cluster ensemble selection based on relative validity indexes

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Cluster ensembles in collaborative filtering recommendation

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Moderate diversity for better cluster ensembles

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From cluster ensemble to structure ensemble

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Fuzzy C-Means clustering of incomplete data based on probabilistic information granules of missing values

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LED: A fast overlapping communities detection algorithm based on structural clustering

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Resampling-based selective clustering ensembles

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Cluster Ensemble Based Image Segmentation Algorithm

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On the Use of a Cluster Ensemble Cloud Classification Technique in Satellite Precipitation Estimation

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Incremental Semi-Supervised Clustering Ensemble for High Dimensional Data Clustering

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Per-Cluster Ensemble Kernel Learning for Multi-Modal Image Clustering With Group-Dependent Feature Selection

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A Mixture Model for Clustering Ensembles

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Fuzzy c -Means and Cluster Ensemble with Random Projection for Big Data Clustering

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Cluster ensemble selection based on a new cluster stability measure1

What points at this work

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