Multi-clustering via evolutionary multi-objective optimization

Wang, Rui; Lai, Shupeng; Wu, Guohua; Xing, Lining; Wang, Ling; Ishibuchi, Hisao

doi:10.1016/j.ins.2018.03.047

Cited by 69 publications

(31 citation statements)

References 29 publications

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“…Sparse representation has been applied in numerous computer vision tasks such as image classification and clustering, and it achieves promising performance [40], [41]. Additionally, multi-objective optimization via meta-heuristic has demonstrated promising results in many applications including clustering analysis [27], [42]. Therefore, in our future work, we would like to explore the potential of these two approaches to clustering mixed data.…”

Section: Discussionmentioning

confidence: 99%

Clustering Mixed Numeric and Categorical Data With Cuckoo Search

Pang

et al. 2020

IEEE Access

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Clustering analysis, as an important technique in data mining, aims to identify the nature groups or clusters of data objects in the attribute space. Data objects in real-world applications are commonly described by both numeric and categorical attributes. In this research, considering that the partitional clustering algorithms designed for this type of mixed data are prone to get trapped into local optima and the cuckoo search approach is efficient in solving global optimization problems, we propose CCS-K-Prototypes, a novel partitional Clustering algorithm based on Cuckoo Search and K-Prototypes, for clustering mixed numeric and categorical data. To deal with different types of attributes, we develop a novel representation for candidate solutions, and suggest two formulas for the cuckoo to search for the potential solution around the existing solutions or in the entire attribute space. Finally, the performance of the proposed algorithm is assessed by a series of experiments on five benchmark datasets. INDEX TERMS Data clustering, cuckoo search, mixed data, numeric and categorical attributes.

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Section: Discussionmentioning

confidence: 99%

Clustering Mixed Numeric and Categorical Data With Cuckoo Search

Pang

et al. 2020

IEEE Access

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“…instead of normalizing the objectives, we use the Schur product to translate fi * (x) to Fi(x) according to the boundary range of the objective values, as (2) where fi'(x)=f(x)-zi min is the ith translated objective value, zi min and zi max are the ith ideal point and the ith nadir point, respectively. The binary operator denotes the Schur product, which takes two matrices of the same dimensions, and produces another matrix where each element is the product of elements of the original two matrices.…”

Section: Multi-scale Nominationmentioning

confidence: 99%

“…Multi-objective optimization problems (MOPs) occur in many real-world applications, in which multiple conflicting objectives need to be solved in order to find a set of optimal [1,2]. Accordingly, the solutions to these MOPs, referred as Pareto-optimal solutions (PS), denote a possible reasonable trade-off between all involved objectives.…”

Section: Introductionmentioning

confidence: 99%

A novel many-objective evolutionary algorithm based on transfer matrix with Kriging model

Wang

Chen

et al. 2020

Information Sciences

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Due to the curse of dimensionality caused by the increasing number of objectives, it is very challenging to tackle many-objective optimization problems (MaOPs). Aiming to alleviate the loss of selection pressure in the fitness evaluation for MaOPs, this paper proposes a novel evolutionary optimization framework, called Tk-MaOEA, based on transfer learning assisted by Kriging model. In this approach, in order to achieve global space optimization, transfer learning is used as a map tool to reduce the objective space, i.e., devising transfer matrix to simplify the optimization process. For the objective optimization, the Kriging model is appropriately incorporated in order to further reduce computation cost. Accordingly, any EA-based paradigm or search strategy can be integrated into this framework. Fast non-dominated sorting and farthest-candidate selection (FCS) methods are used to guarantee the diversity of non-dominated solutions. Comprehensive evaluations on a set of benchmark functions have been conducted to show that the proposed Tk-MaOEA is efficietive for solving complex MaOPs.

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“…That is, clustering itself can be stated as an optimization problem, so it can be solved by the SIO algorithms [54]. Many scholars have been devoted to solving clustering problem using SIO techniques [62][63][64][65][66][67][68][69][70].…”

Section: Comparison Of Clustering Optimization With Six Siomentioning

confidence: 99%

Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

Sun

Chen

et al. 2019

Complexity

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Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.

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Multi-clustering via evolutionary multi-objective optimization

Cited by 69 publications

References 29 publications

Clustering Mixed Numeric and Categorical Data With Cuckoo Search

Clustering Mixed Numeric and Categorical Data With Cuckoo Search

A novel many-objective evolutionary algorithm based on transfer matrix with Kriging model

Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

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