Corpus ID: 2283430

A clustering ensemble framework based on elite selection of weighted clusters

Mathematics, Computer Science

Advances in Data Analysis and Classification

Each clustering algorithm usually optimizes a qualification metric during its progress. The qualification metric in conventional clustering algorithms considers all the features equally important; in… Expand

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44 Citations

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44 Citations

Computer Science

Pattern Analysis and Applications

2017

TLDR

The proposed framework completely overshadows the state-of-the-art clustering ensemble methods experimentally and proposes cluster-level weighting co-association matrix instead of traditional co-Association matrix. Expand2018

TLDR

A criterion is proposed to assess the association between a cluster and a partition which is called Edited Normalized Mutual Information, ENMI criterion and it is shown that the proposed method outperforms other well-known ensembles. ExpandComputer Science

2020

Clustering ensemble indicates to an approach in which a number of (usually weak) base clusterings are performed and their consensus clustering is used as the final clustering. Knowing democratic… Expand

2020

TLDR

A co-clustering ensemble based on bilateral k-means (CEBKM) algorithm that can simultaneously cluster samples and base clusterings of a dataset, and can directly obtain the final clustering results without using other clustering algorithms. Expand2019

TLDR

The unifying framework presented in this paper will help clustering practitioners select the most appropriate weighting mechanisms for their own problems by discussing different types of weights, major approaches to determining weight values, and applications of weighted clustering ensemble to complex data.ExpandComputer Science, Medicine

IEEE Transactions on Neural Networks and Learning Systems

2021

TLDR

This work proposes a novel self-paced clustering ensemble (SPCE) method, which gradually involves instances from easy to difficult ones into the ensemble learning, and proposes a joint learning algorithm to obtain the final consensus clustering result.ExpandComputer Science

MICAI

2014

TLDR

This paper proposes an innovative ensemble creation named the Classifier Selection Based on Clustering (CSBC), which guarantees the necessary diversity among ensemble classifiers, using the clustering of classifiers technique.ExpandComputer Science

IEEE Access

2019

The current research on ensemble clustering mainly focuses on integration strategies, but the attention regarding the measurement and optimization of basic cluster is less emphasized. Based on the… Expand

Computer Science

IEEE Transactions on Fuzzy Systems

2020

TLDR

This article proposes an original algorithm referred to as a hyperplane division method to split the entire data set into disjoint subsets, and validates the proposed strategies on both synthetic and publicly available data to show their superiority over the method of clustering the entireData and over some representative big data clustering methods. ExpandMedicine, Computer Science

IEEE Transactions on Cybernetics

2020

TLDR

This paper proposes a transfer CES (TCES) algorithm which makes use of the relationship between quality and diversity in a source dataset, and transfers it into a target dataset based on three objective functions, and constructs a transfer CE framework based on TCES to obtain better clustering results. Expand...

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References

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Computer Science

CIKM '00

2000

TLDR

A novel clustering technique is proposed, which is based on a supervised learning technique called decision tree construction, that is able to find "natural" clusters in large high dimensional spaces eff iciently and also scales well for large highdimensional datasets. Expand2009

TLDR

This article addresses the problem of combining multiple weighted clusters that belong to different subspaces of the input space to generate a consensus partition that is superior to the participating ones.ExpandComputer Science

SKDD

2004

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A survey of the various subspace clustering algorithms along with a hierarchy organizing the algorithms by their defining characteristics is presented, comparing the two main approaches using empirical scalability and accuracy tests and discussing some potential applications where sub space clustering could be particularly useful. ExpandComputer Science

KDD '99

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This work considers a database with numerical attributes, in which each transaction is viewed as a multi-dimensional vector, and identifies new meaningful criteria of high density and correlation of dimensions for goodness of clustering in subspaces. ExpandComputer Science

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This paper proposes the combination of multiple K-means clusterings using variable k, using cluster lifetime as the criterion for extracting the final clusters; and the adaptation of this approach to string patterns, which leads to a more robust clustering technique. ExpandMathematics, Computer Science

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An algorithm is introduced that discovers clusters in subspaces spanned by different combinations of dimensions via local weightings of features, whose values capture the relevance of features within the corresponding cluster. Expand2008

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A new method for combining hierarchical clustering is proposed and the results show that more accurate results are obtained using hierarchical combination than combination of partitional clusterings. ExpandComputer Science

2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)

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An algorithm for cluster analysis that provides a robust way to deal with datasets presenting different types of clusters and allows finding more than one structure in a dataset by applying a Pareto-based multi-objective genetic algorithm with a special crossover operator. Expand2001

TLDR

This paper addresses the problem of finding consistent clusters in data partitions, proposing the analysis of the most common associations performed in a majority voting scheme, and evaluating the proposed methodology in the context of k-means clustering, a new clustering algorithm being presented. ExpandComputer Science, Mathematics

IEEE Transactions on Pattern Analysis and Machine Intelligence

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TLDR

A theoretical framework for the analysis of the proposed clustering combination strategy and its evaluation is developed, based on the concept of mutual information between data partitions, for extracting a consistent clustering, given the various partitions in a clustering ensemble. Expand...

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44 Citations

35 References

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