Finding Consistent Clusters in Data Partitions

By Ana L. N. Fred. International Workshop on Multiple Classifier Systems, 2001

318 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Food Science

View Paper: https://www.semanticscholar.org/paper/95e6c92af0e9a9712fe6f06d11909c51e68bd3ba

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Citations in the corpus

OpenEnsembles: A Python Resource for Ensemble Clustering (2018, methodology): “An ensemble approach (Fred, 2001) to find stable and optimal solutions from the combination of many solutions of the non-deterministic k-means algorithm with majority vote.”
Using diversity in cluster ensembles (2004, methodology): “Perhaps the simplest implementation of the generic cluster ensemble algorithm is as follows (called " voting c-means algorithm " in [5] and " evidence accumulation algorithm " in [6] Join in the same cluster all the points whose pairwise entry in M is greater than θ.”
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization (2006, methodology): “; L) [9], [32], [37], the feature-based approach (treating outputs from the clusterers as L categorical features and building a clusterer thereupon) [35], the hyper-graph approach (constructing a hyper-graph representing the total output from the clusterers and cutting the redundant edges) [32], and”
Advances in Fuzzy Clustering and its Applications (2007, methodology): “Evidence Accumulation ( Fred and Jain 2001, 2002 ) is a simple framework, very similar to the cluster ensemble framework, for combining the results of multiple weak clusterings in order to increase robustness of the final solution.”
DUET: integration of dynamic and static analyses for malware clustering with cluster ensembles (2013, background): “Fed [10] and Jain [11] constructed the consensus function as a co-association matrix which represents the association or connectivity be-tween each pair of data samples.”
Combining multiple clusterings using evidence accumulation (2005, methodology): “An improvement of the well-known SL method, by exploring nearestneighbor relationships, leads to a more efficient algorithm than the one introduced in [53], [54], [55], and extends the rangeof applicability of theproposed technique to largerdata sets.”
Clustering ensembles: models of consensus and weak partitions (2005, background): “Several recent independent studies [10], [12], [14], [15], [43], [47] have pioneered clustering ensembles as a new branch in the conventional taxonomy of clustering algorithms [26], [27].”
A Mixture Model for Clustering Ensembles (2004, methodology): “Strehl and Ghosh [30] have considered three different consensus functions for ensemble clustering: one is similar to the evidence accumulation approach [11, 12], and the other two methods are based on a hypergraph representation.”
Combining multiple weak clusterings (2003, methodology): “This is the same as the co-association value introduced in [2].”