An evolutionary many-objective approach to multiview clustering using feature and relational data

José-García, Adán; Handl, Julia; Gómez-Flores, Wilfrido; Garza-Fabre, Mario

doi:10.1016/j.asoc.2021.107425

Cited by 19 publications

(18 citation statements)

References 29 publications

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“…On the one hand, data views may correspond to multiple feature spaces characterizing the same entities. For example, José-García et al consider an application to breast tumor classification where five separate feature spaces describe different aspects of ultrasound images [10]. Analogously, Devagiri et al use independent subsets of features to define views describing the operation, performance, and context of the heating and tapwater subsystems in the domain of smart buildings [11].…”

Section: B Multi-view Clusteringmentioning

confidence: 99%

“…Liu et al [16], for example, use both the Euclidean distance and the path distance [17] as distinct data views to increase robustness when dealing with a range of data properties (e.g., spherically or irregularly shaped clusters). Based on a similar rationale, José-García et al [10] explore the use of the Euclidean, cosine, and maximum edge [18] distances as separate data views.…”

Section: B Multi-view Clusteringmentioning

confidence: 99%

“…The use of multiple similarity measures as data views is particularly relevant in scenarios where defining feature spaces is not straightforward [10], [19], and there is significant ambiguity in how best to define similarity. For this reason, MVC has seen significant use in the analysis of web-search results.…”

Section: B Multi-view Clusteringmentioning

confidence: 99%

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Evolutionary Multiobjective Clustering Over Multiple Conflicting Data Views

Garza-Fabre

Handl

José-García

2023

IEEE Trans. Evol. Computat.

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Multi-view data analysis provides an effective means to integrate the distinct information sources which are inherent to many applications. Data clustering in a multi-view setting specifically aims to identify the most appropriate grouping for a collection of entities, where those entities (or their relationships) can be described from multiple perspectives. Leveraging recent advances in multi-objective clustering, we propose a new evolutionary method to tackle this challenge. Designed around a flexible and unbiased solution representation, together with strategies based on the minimum spanning tree and neighborhood relations, our algorithm optimizes multiple objectives simultaneously to effectively explore the space of candidate trade-offs between the data views. Through a series of experiments, we investigate the suitability of our proposal in the context of a bioinformatics application, clustering of plausible protein structures, and a diverse set of synthetic problems. The specific case of two data views is considered in this paper. The evaluation with respect to a variety of reference approaches demonstrates the effectiveness of our method in discovering high-quality partitions in a multiview setting. Robustness against unreliable data sources and the ability to automatically determine the number of clusters, are additional advantages evidenced by the results obtained.

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Section: B Multi-view Clusteringmentioning

confidence: 99%

Section: B Multi-view Clusteringmentioning

confidence: 99%

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Evolutionary Multiobjective Clustering Over Multiple Conflicting Data Views

Garza-Fabre

Handl

José-García

2023

IEEE Trans. Evol. Computat.

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“…For instance, multiple data matrices might be available for the same set of genes and conditions. In this regard, multi-view data clustering algorithms can integrate these information pieces to find consistent clusters across different data views [34,35]. This same multi-view clustering concept can be extended to biclustering, where the aim is to discover biclusters across multiple data matrices (i.e., data views).…”

Section: Future Directionsmentioning

confidence: 99%

Biclustering Algorithms Based on Metaheuristics: A Review

José-García¹,

Jacques²,

Sobanski³

et al. 2022

Preprint

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Biclustering is an unsupervised machine learning technique that simultaneously clusters rows and columns in a data matrix. Biclustering has emerged as an important approach and plays an essential role in various applications such as bioinformatics, text mining, and pattern recognition. However, finding significant biclusters is an NP-hard problem that can be formulated as an optimization problem. Therefore, different metaheuristics have been applied to biclustering problems because of their exploratory capability of solving complex optimization problems in reasonable computation time. Although various surveys on biclustering have been proposed, there is a lack of a comprehensive survey on the biclustering problem using metaheuristics. This chapter will present a survey of metaheuristics approaches to address the biclustering problem. The review focuses on the underlying optimization methods and their main search components: representation, objective function, and variation operators. A specific discussion on single versus multi-objective approaches is presented. Finally, some emerging research directions are presented.

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“…In a closely related work [6], however, the authors replace index I, adopting the silhouette index instead [30]. Other examples of the use of the silhouette index for decision-making purposes include algorithm MOVGA (multiobjective variable string length genetic fuzzy clustering) [31] and algorithm MVMC (multi-view multiobjective clustering) [7].…”

Section: ) Decision Making Based On Additional Clustering Criteriamentioning

confidence: 99%

Decision Making in Evolutionary Multiobjective Clustering: A Machine Learning Challenge

Garza-Fabre¹,

Sánchez-Martínez²,

Aldana-Bobadilla

et al. 2022

IEEE Access

Self Cite

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Evolutionary multiobjective algorithms have become a popular choice to tackle the clustering problem. On the one hand, the simultaneous optimization of complementary clustering criteria offers an increased robustness to changes in data characteristics. On the other hand, the evolutionary search is able to approximate the Pareto optimal front and deliver a set of trade-offs between these criteria in a single algorithm execution. Decision making is the concluding stage of the pipeline, having as its goal the selection of a single, final solution from the set of candidate trade-offs produced. This is a complex task for which a definitive answer does not seem to be available, as the underlying assumptions of existing techniques may not hold for all applications. In this paper, we investigate an alternative approach to address this challenge: posing it as a learning problem. The key idea is to build a model that, given a proper characterization of solutions and their context (defined by the full approximation solution set and the specific clustering task at hand), is able to estimate quality and facilitate the identification of the best choice. To evaluate the suitability of this approach, we conduct a series of experiments over diverse synthetic and real-world datasets, including comparisons against a range of representative decision-making strategies from the literature. Our proposal exhibits greater flexibility in dealing with problems of varying characteristics, consistently outperforming the reference methods considered. This study demonstrates that it is possible to learn from the decisionmaking process in example settings and generalize the acquired knowledge to new scenarios.

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An evolutionary many-objective approach to multiview clustering using feature and relational data

Cited by 19 publications

References 29 publications

Evolutionary Multiobjective Clustering Over Multiple Conflicting Data Views

Evolutionary Multiobjective Clustering Over Multiple Conflicting Data Views

Biclustering Algorithms Based on Metaheuristics: A Review

Decision Making in Evolutionary Multiobjective Clustering: A Machine Learning Challenge

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