Clustering of multi-view relational data based on particle swarm optimization

Gusmão, Renê Pereira de; Carvalho, Francisco de A. T. de

doi:10.1016/j.eswa.2018.12.053

Cited by 18 publications

(6 citation statements)

References 33 publications

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“…The study performed in [7] proposed hard clustering algorithms based on PSO for the clustering of multi-view relational data. The proposed methods use a centralized approach in which all views are used simultaneously in the clustering process.…”

Section: Related Workmentioning

confidence: 99%

A distributed approach to cluster multi-view relational data

Gusmão,

Araújo,

Carvalho

2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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Clustering of multi-view data has become an important research field. The efficient clustering of multi-view data is a challenging problem. This work aimed to investigate a distributed approach to cluster multi-view relational data. A PSO-based hybrid method was used to generate clustering from all views independently. Five different objective functions were explored to induce diversity to the clusterings since each function looks for different cluster structures. Five different consensus functions were compared to produce the final partition from the ensembles. Three multi-view real-world data sets were considered in this study. The Adjusted Rand Index, the F-measure and Silhouette clustering validity indexes were used to assess obtained clusterings. The distributed approach found better clusterings for all data sets considering at least one consensus function.

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Section: Related Workmentioning

confidence: 99%

A distributed approach to cluster multi-view relational data

Gusmão,

Araújo,

Carvalho

2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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“…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%

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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“…Another example of combining particle swarm optimization with the hybrid clustering method is found in [20], which enjoys the benefit of the global convergence ability by particle swarm optimization and the local exploitation of hard clustering algorithms. An employment of the particle swarm optimization algorithm for the task of clustering time-series data is reported in [21].…”

Section: Dimension Reduction In Swarm Intelligence Recommendation Setmentioning

confidence: 99%

Improving Ant Collaborative Filtering on Sparsity via Dimension Reduction

Liao

et al. 2020

Applied Sciences

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Recommender systems should be able to handle highly sparse training data that continues to change over time. Among the many solutions, Ant Colony Optimization, as a kind of optimization algorithm modeled on the actions of an ant colony, enjoys the favorable characteristic of being optimal, which has not been easily achieved by other kinds of algorithms. A recent work adopting genetic optimization proposes a collaborative filtering scheme: Ant Collaborative Filtering (ACF), which models the pheromone of ants for a recommender system in two ways: (1) use the pheromone exchange to model the ratings given by users with respect to items; (2) use the evaporation of existing pheromone to model the evolution of users’ preference change over time. This mechanism helps to identify the users and the items most related, even in the case of sparsity, and can capture the drift of user preferences over time. However, it reveals that many users share the same preference over items, which means it is not necessary to initialize each user with a unique type of pheromone, as was done with the ACF. Regarding the sparsity problem, this work takes one step further to improve the Ant Collaborative Filtering’s performance by adding a clustering step in the initialization phase to reduce the dimension of the rate matrix, which leads to the results that K<<#users, where K is the number of clusters, which stands for the maximum number of types of pheromone carried by all users. We call this revised version the Improved Ant Collaborative Filtering (IACF). Experiments are conducted on larger datasets, compared with the previous work, based on three typical recommender systems: (1) movie recommendations, (2) music recommendations, and (3) book recommendations. For movie recommendation, a larger dataset, MoviesLens 10M, was used, instead of MoviesLens 1M. For book recommendation and music recommendation, we used a new dataset that has a much larger size of samples from Douban and NetEase. The results illustrate that our IACF algorithm can better deal with practical recommendation scenarios that handle sparse dataset.

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Clustering of multi-view relational data based on particle swarm optimization

Cited by 18 publications

References 33 publications

A distributed approach to cluster multi-view relational data

A distributed approach to cluster multi-view relational data

Evolutionary Multiobjective Clustering Over Multiple Conflicting Data Views

Improving Ant Collaborative Filtering on Sparsity via Dimension Reduction

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