An Improved and More Scalable Evolutionary Approach to Multiobjective Clustering

Gong

et al. 2021

IEEE Trans. Cybern.

In many practical applications, it is crucial to perform automatic data clustering without knowing the number of clusters in advance. The evolutionary computation paradigm is good at dealing with this task, but the existing algorithms encounter several deficiencies, such as the encoding redundancy and the cross-dimension learning error. In this article, we propose a novel elastic differential evolution algorithm to solve automatic data clustering. Unlike traditional methods, the proposed algorithm considers each clustering layout as a whole and adapts the cluster number and cluster centroids inherently through the variable-length encoding and the evolution operators. The encoding scheme contains no redundancy. To enable the individuals of different lengths to exchange information properly, we develop a subspace crossover and a two-phase mutation operator. The operators employ the basic method of differential evolution and, in addition, they consider the spatial information of cluster layouts to generate offspring solutions. Particularly, each dimension of the parameter vector interacts with its correlated dimensions, which not only adapts the cluster number but also avoids the cross-dimension learning error. The experimental results show that our algorithm outperforms the state-of-the-art algorithms that it is able to identify the correct number of clusters and obtain a good cluster validation value.

Section: ) Clustering With Fixed Number Of Clustersmentioning

confidence: 99%

Elastic Differential Evolution for Automatic Data Clustering

Gong

et al. 2021

IEEE Trans. Cybern.

“…Many state-of-theart MOEAs, such as NSGA-II [8], SPEA2 [53], IMOEA [41], and MOEA/D [15,51], have been proposed to handle multiobjective optimization problems (MOPs). They also have been used in the field of data mining successfully, such as clustering [10,18], classification [11,25] and feature selection [27,45]. MOEA-based clustering algorithms focus on using multiple criterions, such as the cluster validity indexes, to capture the characteristics of the data [16,26].…”

Section: The Multiobjective Evolutionary Algorithm Based Clusteringmentioning

confidence: 99%

A pareto ensemble based spectral clustering framework

Luo

Zhou³

2020

Complex Intell. Syst.

Similarity matrix has a significant effect on the performance of the spectral clustering, and how to determine the neighborhood in the similarity matrix effectively is one of its main difficulties. In this paper, a “divide and conquer” strategy is proposed to model the similarity matrix construction task by adopting Multiobjective evolutionary algorithm (MOEA). The whole procedure is divided into two phases, phase I aims to determine the nonzero entries of the similarity matrix, and Phase II aims to determine the value of the nonzero entries of the similarity matrix. In phase I, the main contribution is that we model the task as a biobjective dynamic optimization problem, which optimizes the diversity and the similarity at the same time. It makes each individual determine one nonzero entry for each sample, and the encoding length decreases to O(N) in contrast with the non-ensemble multiobjective spectral clustering. In addition, a specific initialization operator and diversity preservation strategy are proposed during this phase. In phase II, three ensemble strategies are designed to determine the value of the nonzero value of the similarity matrix. Furthermore, this Pareto ensemble framework is extended to semi-supervised clustering by transforming the semi-supervised information to constraints. In contrast with the previous multiobjective evolutionary-based spectral clustering algorithms, the proposed Pareto ensemble-based framework makes a balance between time cost and the clustering accuracy, which is demonstrated in the experiments section.

“…In other words, the learning system perceives and categorizes persistent input vectors without any feedback from the environment in particular an external supervisor or critic. Thus this type of learning is popularly and frequently employed for data clustering [3,4,5,6], feature extraction [7], and similarity detection [8]. In a nutshell, this paper focuses on developing an unsupervised learning of data clustering.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Functional Link Artificial Neural Networks for Cluster Analysis

et al. 2020

In this paper, we propose a novel method of cluster analysis called unsupervised functional link artificial neural networks (UFLANNs), which inherit the best characteristics of functional link artificial neural networks and self-organizing feature maps (SOFMs). UFLANNs adopt three types of basis functions such as Chebyshev, Legendre orthogonal polynomials, and power series for mapping the input data into a new feature space with higher dimensions, where the objects are clustered based on the principle of competitive learning of SOFMs. The effectiveness of this algorithm has been tested with various artificial and real-life datasets including remote sensing images. A thorough comparison with other popular clustering algorithms shows that the proposed method is promising in revealing clusters from many complex datasets.