Clustering aggregation

By Aristides Gionis, Heikki Mannila, Panayiotis Tsaparas. IEEE International Conference on Data Engineering, 2005

1,050 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Organizational Behavior and Human Resource Management, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0e803afc7790fb7ffd95b129d300d5d778ec33a4

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Citations in the corpus

Ensembles for unsupervised outlier detection: challenges and research questions a position paper (2014, methodology): “, using similarity measures (again, as those available from external validity measures) or more refined methods of deriving some consensus partition [67; 19; 5; 69; 68; 21; 25; 55].”
Study on density peaks clustering based on k-nearest neighbors and principal component analysis (2016, background): “The second data set, aggregation [29] consists of the seven perceptually distinct groups of points where there are non-Gaussian clusters.”
Distributed Clustering in the Anonymized Space with Local Differential Privacy (2019, methodology): “In the visualization part, we use three publicly available datasets: blobs based (Aggregation dataset [27]),circles based (pathbased dataset [28]) and moonshape based dataset (“twomoons” [29]).”
Aggregating inconsistent information: ranking and clustering (2005, background): “When the edge weights satisfy the probability constraints (i.e. w + ij + w − ij = 1 for all edges), the best previous approximation factor was 7 [20, 6].”
Rank Aggregation: Together We're Strong (2009, background): “Some papers that investigate the theoretical and practical performance of algorithms for consensus clustering are [14, 15].”
dbscan: Fast Density-Based Clustering with R (2019, background): “0 CHAMELEON algorithm (Karypis, Han, and Kumar 1999), along with a variety of shape data sets commonly found in clustering validation papers (Gionis, Mannila, and Tsaparas 2007; Zahn 1971; Chang and Yeung 2008; Jain and Martin 2005; Fu and Medico 2007).”
Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors (2016, methodology): “The synthetic datasets used come from research published in [3,10,11,13,14,27] and are commonl used benchmark datasets to test the performance of clustering algorithms.”
Learning similarity metrics for event identification in social media (2010, background): “Ensemble clustering is an approach that combines multiple clustering solutions for a document set [17, 18, 34].”
A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data (2015, neutral): “A diverse set of similarity measures for continuous data was studied on low and high-dimensional continuous datasets in order to clarify and compare the accuracy of each similarity measure in different datasets with various dimensionality situations and using 15 datasets [18,19,46 – 49].”
Knowledge transfer via multiple model local structure mapping (2008, methodology): “In unsupervised learning, many clustering ensemble methods [12, 17, 25, 26] have been developed to find a consensus clustering from multiple partitionings without accessing the features.”
DUET: integration of dynamic and static analyses for malware clustering with cluster ensembles (2013, methodology): “According to [28], it is a botnet program that terminates if it detects being executed in a virtual environment. to ﬁnd the ﬁnal clustering, C , that shares the most commonality with the constituent clusterings [12].”
Large-Scale Deduplication with Constraints Using Dedupalog (2009, methodology): “However, we note that alternate approaches to clustering require expensive precomputation such as computing a solution to a linear program [26], [25], or requires computing several clusterings [30], which can be arbitrarily slower.”