CoFKM: A Centralized Method for Multiple-View Clustering

Cleuziou, Guillaume; Exbrayat, Matthieu; Martin, Lionel; Sublemontier, Jacques-Henri

doi:10.1109/icdm.2009.138

Cited by 91 publications

(44 citation statements)

References 8 publications

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“…In addition to the weight parameter, the method has another drawback: it constrains the number of hidden topics in text and tag sources to be the same, which is a strong assumption on data that is not always true. Considering the multi-view clustering problem Cleuziou et al [9] propose to find a consensus between the clusters from different views. Their approach merges information from each view by performing a fusion process that identifies the agreement between the views and solves the conflicts.…”

Section: Related Workmentioning

confidence: 99%

Parameter-less co-clustering for star-structured heterogeneous data

Ienco

Robardet

Pensa

et al. 2012

Data Min Knowl Disc

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The availability of data represented with multiple features coming from heterogeneous domains is getting more and more common in real world applications. Such data represent objects of a certain type, connected to other types of data, the features, so that the overall data schema forms a star structure of inter-relationships. Co-clustering these data involves the specification of many parameters, such as the number of clusters for the object dimension and for all the features domains. In this paper we present a novel co-clustering algorithm for heterogeneous star-structured data that is parameter-less. This means that it does not require either the number of row clusters or the number of column clusters for the given feature spaces. Our approach optimizes the Goodman-Kruskal's τ , a measure for cross-association in contingency tables that evaluates the strength of the relationship between two categorical variables. We extend τ to evaluate co-clustering solutions and in particular we apply it in a higher dimensional setting. We propose the algorithm CoStar which optimizes τ by a local search approach. We assess the performance of CoStar on publicly available datasets from the textual and image domains using objective external criteria. The results show that our approach outperforms state-of-the-art methods for the co-clustering of heterogeneous data, while it remains computationally efficient.

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Section: Related Workmentioning

confidence: 99%

Parameter-less co-clustering for star-structured heterogeneous data

Ienco

Robardet

Pensa

et al. 2012

Data Min Knowl Disc

View full text Add to dashboard Cite

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“…objects or attributes values) and trying to find clusters along this dimension taking into account what is found by the other agent. More recent work on co-clustering, which is not in the scope of this paper, can be found in [49,50,51].…”

Section: Related Workmentioning

confidence: 98%

Collaborative clustering: Why, when, what and how

et al. 2018

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Highlights• A didactic presentation of issues raised by clustering is given.• An up-to-date review and classi cation of collaborative clustering methods is presented.• The questions about why, when and how collaborative clustering can help are addressed. AbstractClustering is one type of unsupervised learning where the goal is to partition the set of objects into groups called clusters. Faced to the difficulty to design a general purpose clustering algorithm and to choose a good, let alone perfect, set of criteria for clustering a data set, one solution is to resort to a variety of clustering procedures based on different techniques, parameters and/or initializations, in order to construct one (or several) final clustering(s). The hope is that by combining several clustering solutions, each one with its own bias and imperfections, one will get a better overall solution.In the cooperative clustering model, as Ensemble Clustering, a set of clustering algorithms are used in parallel on a given data set: the local results are combined to get an hopefully better overall clustering. In the collaborative framework, the goal is that each local computation, quite possibly applied to distinct data sets, benefit from the work done by the other collaborators. This paper is dedicated to collaborative clustering. In particular, after a brief overview of clustering and the major issues linked to, it presents main challenges related to organize and control the collaborative process.

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“…Another issue is multi-view data, where the objects are represented by several (feature or relational) data matrices. Multi-view data can be found in many domains such as bioinformatics, marketing, etc [5].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy C-Medoids Clustering Algorithm Based on Multiple Dissimilarity Matrices

Carvalho

Melo

Lechevallier

2013

2013 Brazilian Conference on Intelligent Systems

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This paper gives a relational fuzzy c-medoids clustering algorithm that is able to partition objects taking into account simultaneously several dissimilarity matrices. The aim is to obtain a collaborative role of the different dissimilarity matrices in order to obtain a final consensus partition. These matrices could have been obtained using different sets of variables and dissimilarity functions. This algorithm is designed to give a fuzzy partition and a prototype for each fuzzy cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an objective function. These relevance weights change at each algorithm's iteration and are different from one cluster to another. Several examples illustrate the usefulness of the proposed algorithm.

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CoFKM: A Centralized Method for Multiple-View Clustering

Cited by 91 publications

References 8 publications

Parameter-less co-clustering for star-structured heterogeneous data

Parameter-less co-clustering for star-structured heterogeneous data

Collaborative clustering: Why, when, what and how

A Fuzzy C-Medoids Clustering Algorithm Based on Multiple Dissimilarity Matrices

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